Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.

  • Novel integration of geographic information system, remote sensing, and EPANET for water distribution system analysis.

  • Focus on water distribution challenges in Bota town.

  • Improved system efficiency and equitable water distribution as anticipated outcomes.

  • Practical applications for water utilities and policymakers in water resource management.

  • Replicable methodology with potential for global applicability.

Providing potable water has been a significant challenge throughout human history in terms of both quantity and quality, as most historical civilizations reside near water sources. Water is an indispensable natural resource that determines human habitation and the quality of life. Despite its abundance on Earth, the availability and quality of water do not always meet everyone's requirements. The equilibrium between water supply and demand is crucial, and the availability of potable water controls the development level of any region (Gober et al. 2010). If the water supply cannot meet the demand, further development in a region is not sustainable. In India, water management has been traditionally done by the construction of large dams and canal systems to increase the availability of pure water to satisfy the expanding demand of consumers. With the limited water supply and established spread-out urban infrastructure of the 21st century, a more effective demand management is necessary to ensure a sustainable water supply. The evaluation of population growth and the efficacy of alternative water supply network techniques, management policies, regulations, and conservation activities can benefit from water-use data (Karadirek et al. 2012).

Water is a non-renewable resource with limited availability and ever-growing utilization. The urban water supply has become vulnerable due to factors such as high population density, expanding urbanization, economic growth, and climate change. An efficient infrastructure has been required to provide pure potable water to consumers. Urban water cycle components include collection, treatment, and discharge of used water back into the environment (Oviedo-Ocaña et al. 2020). Water collection, treatment, storage, and distribution to residential, commercial, and industrial consumers comprise the water supply system (Peng et al. 2020). Sustainable development necessitates adapting and mitigating the effects of climate change, which can present difficulties in urban and peri-urban water provision, such as modifying water consumption patterns and devising emergency plans (Abdy Sayyed et al. 2015). New strategies and technologies, including digital water solutions, are required to address these challenges. The European model for a water-wise society entails a paradigm transition towards sustainable and climate-resilient water management. A geographic information system (GIS) is a system that collects, stores, incorporates, and analyses geographically referenced data (Fernández Moniz et al. 2020). GIS-based water network permits the identification of spatial relationships between map features and different attributes pertaining to related properties of the spatial object on Earth's surface. The fundamental ability of GIS has disclosed the relationships between features by superimposing remotely sensed maps of water entitlements, river networks, and river basins in water availability modelling research (Panagopoulos et al. 2012).

The urban sprawl and rise in populations have increased the difficulty of meeting end user's water demands. To resolve these issues, well-planned steps should be taken to develop a system that is effective, regular, and well managed (Mohapatra et al. 2014). In this article, an attempt has been made to analyse the administration of Bota town domestic water supply network performance using EPANET and GIS software. Bota is a swiftly expanding metropolis in India's Maharashtra state. The town has encountered difficulties in providing secure and pure potable water to its growing population. The city's existing water distribution system is outdated and must be modernized to satisfy the rising demand for water. In this study, geo-spatial features of piped network and EPANET-based simulations have been used to analyse and design water distribution systems. The purpose of our research is to create a map of Bota town's water distribution zone and examine the traditional method of domestic water supply management system using EPANET for a region.

The ground level collected municipality data has been superimposed on ARC GIS software to produce zonal maps of the water distribution network as a part of the research methodology. The raster image obtained from Google Earth Engine has been georeferenced with toposheet coordinates produced on the map. The boundaries of each settlement zone, water supply data, and schedules of elevated surface reservoirs (ESR) have been outlined on the geo-spatial map. The vector images of the piped network and digital elevation model (DEM) have been used to create the final thematic map. The distribution system has been analysed using EPANET software, which provides pipe and junction information. The authors emphasized the significance of utilizing hydraulic modelling software, such as EPANET, to simulate the passage of water in the distribution network and evaluate the efficacy of the network under various circumstances (Venkata Ramana & Sudheer Chekka 2018). With the ESR data plotted, the creation of the GIS-based map has been crucial in allowing the accurate identification of every urban settlement zone, water demands, and network concerns. Without GIS-based simulated maps, water distribution system design and analysis would have been far more challenging. The research methodology has been effective in analysing and designing the water distribution system of Bota town using GIS and RS tools.

This research article emphasizes the importance of utilizing hydraulic modelling software such as EPANET, to simulate the flow of water in the distribution network and evaluate its efficacy under a variety of circumstances. EPANET can assist in identifying a variety of issues, including low-water pressure, inefficient distribution, and water contamination. EPANET can assist in identifying strategies to enhance water quality and distribution, increase efficiency, and reduce water loss by analysing and modelling water distribution systems. This article examines previous studies that utilized remote sensing (RS), GIS, and EPANET to optimize water distribution networks, evaluate the influence of network design on water quality, and identify improvement areas.

Many studies have used geo-spatial images of the water distribution network, assessed its performance, and prioritized improvement opportunities. Pioneer research goals are to digitize land use maps at a scale of 1:25,000, to determine the availability of natural resources, and to create an optimum land use plan. RS and GIS have identified environmentally sensitive regions for land use planning in urban and rural areas (Sharma et al. 2020a, 2023; Singh et al. 2023). Understanding the topography and its features is essential for land use planning. The wetland rice ecosystem was underscoring the need to protect these catchment regions of mostly thick and open forests for land use planning.

The USEPA's total maximum daily load program uses GIS and RS techniques to control the water quality. RS and GIS have made water quality monitoring more efficient and cost-effective. Soft computing techniques can help water resource managers and decision-makers detect and map pollution sources and improve water quality. Soil Conservation Service Curve Number (SCS-CN) model, remote sensing, and GIS helped in estimating watershed discharge depth without gauge stations. This strategy helps manage and plan water resources in water-scarce municipalities at low cost (Nagarajan & Poongothai 2012). An improved user interface can manage input choices including contour-based node elevation calculation. GIS has been used to design water distribution networks that supply potable water across huge regions at the right volumes and pressures. AVENUE and the Dialogue Designer Extension in Arcview3.1 have been used to create database-supported mapping interfaces (Qasem & Jamil 2021).

RS and GIS can categorize crops, estimate rainfall and snowfall, analyse soil moisture, and utilize surface and groundwater, for water resource management. The geographical dataset might improve our understanding of the hydrology of data-scarce transboundary basins with considerable climate variability (Kumar et al. 2023a, 2023b). An optimization model for pipe diameter, together with other layers such as the city's topography layer, has helped to understand network behaviour and identified critical zones in urban areas. Multi-criteria decision analysis and GIS techniques have calculated the water needs of Mytilene's municipal areas (Lee et al. 2020). Egypt has most of the world's groundwater. Egypt occupies 1 million km2 in northern Africa (Elbeltagi et al. 2020a, 2020b). The 10 most significant characteristics of urban growth and water demand have been evaluated with a potential urban water demand map using GIS and Analytic Hierarchy Process (AHP) findings. The suggested technique was validated by spatially correlating the map with water usage and active water connection variation maps.

AutoCAD 2000 has been used for drafting the hypothetical water distribution system's water CAD design. Water CAD's hydraulic analysis, including junction pressure, pipe flow, and reservoir water level, has been exported and translated into database files using ArcView. This research saves time and cost in the construction of the water supply network. This has been a fast, easy, and flexible method. GIS can easily oversee regular network design difficulties if enough amounts of ground data are available. QGIS and USLE model have estimated the soil erosion for different watershed and water levels. Soil erosion is a complicated dynamic process in which productive soil surface is separated, transported, and stored elsewhere, exposing beneath the soil to atmospheric phenomena. This research evaluates the Khuldabad watershed average annual soil erosion using QGIS and USLE. Erosion rates from 0 to 45 ton/year have been classified under minor, moderate, severe, very severe, and extreme (Muranho et al. 2014b). Predicting future network interventions and budgeting requires knowledge about fractures and renewals. Satellite data may be utilized to create water reports, restrict upstream water resource increase, replenish groundwater, and fairly regulate irrigation (Zohaib et al. 2022).

Monitoring, modernization, rehabilitation, development, and automation are the main components of waterworks systems. This facilitates the delivery of water from its source to the customer by integrating inputs from a spectrum of evolving technological advancements. Hydrological models and GIS analytic tools ease the monitoring and database creation that simplifies water system planning, operation, and maintenance. A water supply network based on the GIS-based system increases the region's technical, economical, and legal control. Free and open-source geographical data, tools, and methods have been used to create flood hazard areas and flood-risk areas. The findings identified flood-prone areas and social damage victims (Natarajan et al. 2021). The groundwater capacity has been estimated because of higher uncertainty in nature and unidentified values in general. Mapping groundwater potential eases the complication of groundwater management planning. These findings aid comprehensive groundwater exploration and management. Open-source software allows users to modify and share its source code. The work may be replicated in different groundwater-refilling sites (Gong et al. 2014). RS and GIS have detected agricultural drought in real time using repeatable, dependable, and free data sources. The research approach may enhance the canals' cycle design by shifting water from low-water to high-water areas. The technique requires open-source satellite images of MODIS/Landsat 8 and weather data at different time intervals (Sharma et al. 2020b, 2021).

A GIS-hydraulic model management system has been used to assess water and sewage networks of city's water and wastewater management system. Researchers examined the integrated system for the hydraulic condition of the water supply network to modify the water level as per required conditions. The GIS model visualized Karrada's water supply and found no defects (Bera et al. 2021). Several tools have been implemented to manage water distribution networks, learn more about their assets, and explain why it is critical to use these tools to investigate network malfunctions and assist maintenance professionals in taking immediate action when problems are discovered, such as a pipe break. Water, waste, and sewers are piped underground. For maintenance and repair, underground pipe networks require financial modelling. These networks perform a variety of functions, including the transportation of water, garbage, and sewage. Research stated that maintenance works that require excavation, such as road or footpath elimination, may present chances for treating numerous facilities within the same trench (Comair et al. 2014).

Water transportation efficiency has a substantial impact on water supply system management. The water distribution system of Tulu Bolo has been evaluated using a hydraulic model of the water distribution network built by combining GIS and WaterGEMS. The water distribution line failures were simulated to emphasize the importance of water adapting its flow and speed (Muranho et al. 2014a). They compared pressure distribution in this network in both fault-free and fault-prone conditions. Thus, consequences of each broken pipe may be established by examining the area where the pressure dropped below the acceptable level, how long it lasted, and how many people were affected by the reduced water supply (Sujatha & Sridhar 2018).

A GIS-integrated DRastic model was used to undertake a complete assessment of groundwater vulnerability in the Nangasai basin. The assessment took seven hydrogeological parameters into account and revealed five susceptibility groups, with DRastic risk scores ranging from 74 to 198. Notably, 16.80 and 21.38% of the study region had extremely high and highly vulnerable circumstances, respectively, whereas 18.21 and 27.49% had extremely low and low vulnerability levels (Keyantash & Dracup 2004; Bera et al. 2021). The study looked at the impact of groundwater quality on the water supply network in the southeast of Illizi, Algeria. The pH, total dissolved solids, total hardness, total alkalinity, and temperature of 10 existing wells, all of which delivered drinkable lower Devonian water, were measured. The impact of groundwater was also analysed using indicators such as the Langelier stability index (LSI), Ryznar stability index (RSI), and aggressivity index (AI). Lower Devonian groundwater has low LSI, RSI, and AI values in all 10 samples, indicating under-concentration. Despite mild activity estimates, the spatial distribution of the Ryznar index was mapped using standard kriging, revealing an increase in groundwater saturation levels at wells (Figueiredo et al. 2021).

The findings of this study on Goma Township's water supply have the potential to be applied to other African communities. The graphical representation of the network's status provides vital information to managers and decision-makers, allowing them to make more educated investment and repair decisions. This testing strategy has the potential to drastically lower the costs of improving water supply networks in sub-Saharan Africa while improving overall efficiency. Furthermore, if aspects such as water quality and energy concerns are incorporated in multi-dimensional evaluations, the research could serve as a foundation for comparative assessments of water supply networks in African cities (Boryczko et al. 2021; Ciraane et al. 2022).

Rapid development, population growth, water scarcity, climate change, and economic growth are all driving factors in water recovery. This study investigates the prior research on developing a water transport system to give recycled water to industry to reduce water consumption. As a result, wastewater reuse is critical for urban and industrial infrastructure as well as water recovery. It must also create low-cost ways to filter water for commercial reuse. A stable water supply network is required for recycled water. Using the hydraulic model, GIS was utilized to build and display the network of the water distribution system. To fully utilize recycled water and supply it to people via a water distribution system, a few extra factors must be considered, including public opinion, cost, technical feasibility, and health concerns.

The review of literature has given insights into water distribution system modelling studies and emphasises the importance of GIS, remote sensing, and EPANET in evaluating and enhancing such systems. The review assists the writers in examining Bota town's water distribution system and making recommendations for improvements. According to the research study, GIS and RS can be used to manage home water supplies by EPANET, ArcGIS, Global Mapper, Google Earth, ArcView, and QSWATMOD.

The research methodology for this investigation can be divided into four stages. First, data collection and mapping of geo-spatial data from a variety of sources, including satellite imagery, DEMs, hydrologic data, and the extant water supply network. Second, the mapped hydrologic data consist of water sources and demand centres in the town. Third, the water distribution system has been modelled in EPANET to simulate the network's hydraulic behaviour under various operating conditions. The simulation results have been analysed to identify areas of low pressure, excessive head loss, and network leaks. Four, design alterations and modifications have been proposed to enhance the efficacy of the water distribution system based on the simulation results analysis. Figure 1 shows the geographical location of the study area.
Figure 1

Study area location.

Figure 1

Study area location.

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The methods used in computations to establish the required water demand and flow rates for the purpose of developing and analysing water distribution systems are depicted in Figure 2.
Figure 2

Flowchart of research methodology.

Figure 2

Flowchart of research methodology.

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Figure 2 shows the methodology of calibrating the hydraulic model by comparing the calculated results with the real pressures and flows before applying it for the analysis. The adjusted parameters are pipe roughness values, pipe diameter, demand patterns, pipe length, and node elevation data. Pipe roughness is very important among these, while the pipe diameter plays a role in affecting the flow rates as the real internal diameter might be much smaller due to the internal corrosion of metallic pipes. The input data have been pipe diameter, length, pressure readings, and per capita demand and are obtained from real-world measurements of an existing system through a municipal body. This is then modelled in the EPANET water distribution system. Setting parameter values based on available data is the first step of the calibration process. Next, by using these initial settings, EPANET simulations are performed, and the results are closely compared to the data that were observed. If the model meets the requirements, the process is finished; if not, the parameters are changed iteratively.

The study calculates the required water demand in litres per capita per day (LPCD) for the Bota town population. The computation considers factors such as population size (P), Per Capita Requirement (PCR), and peak factor (f), which is a multiplier used to account for spikes in water demand during specific times of day, such as the morning and evening hours. The research calculation equations allow for the computation and approximation of water demand and flow rates, which are critical in the creation and evaluation of Bota town's water distribution systems using GIS, RS, and EPANET. This work makes sure that the infrastructure is structured in such a way that the necessary water needs are met in the most efficient and effective way possible. EPANET does long-term simulations of water and water quality travel through pressure pipe networks. The modelling procedure includes data points such as pipe flow, junction pressure, pollutant dispersion, chlorine concentration, water retention duration, and scenario exploration. This simplifies the calculation of pumping energy and expenses, allowing for the modelling of various valve types such as shutoffs, check pressure regulators, and flow controllers. The application can be found at www.epa.gov/water-research/epanet. Figure 3 depicts the modelled input integrated images of a GIS-based water supply network.
Figure 3

Modelled input data.

Figure 3

Modelled input data.

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The Bota's Nashik Road zone has been chosen for analysis, specifically in terms of identifying suitable locations for ESR based on the tank analysis, determining distributaries based on junction or node analysis, locating or laying pipes based on pipe analysis, and running a final network analysis to obtain various water distribution system outputs. The study and evaluation of the distribution system requires considering a variety of elements, including node and link characteristics, as well as pipe details. This includes calculating discharge in the pipe, flow velocity, and distribution system unit head loss. The graphs illustrating base demand and velocity distribution show the former's temporal fluctuation. The data used in this investigation are obtained from Google Earth Engine satellite images and RS data. A new map has been created for each ESR, including information on the pipeline network, pipe sizes, number of valves and their dimensions, and other characteristics. The goal of our research is to obtain data from the Bota municipality residents to various zones inside Bota town, followed using RS software to georeference the acquired information. Bota's cartographic representation has been obtained from reports made by Bota Municipal Corporation.

The proposed methodology entails gathering demographic data from the Bota Town Planning Department for each zone, followed by digitalization of the data as zonal maps with QGIS. The goal is to analyse the current residential water supply system and identify any potential problems that may have occurred because of the previous governing body's actions. The spatial coordinates and graphical data from the satellite images have been merged with other pertinent information like base demand, velocity distribution, and pipe properties. The coordinates of sites within Bota town have been extracted and used as ground control points (GCPs). With the addition of GCP, the transformation settings have been modified to use EPSG:4326-WGS 84/UTM zone 43N as the reference system for the RS image of Bota town.

Bota is situated on the banks of the Kach River, precisely at latitude 19°33′48.34″ N and longitude 74°12′31.04″ E. The town has a population of 7,685 people and a land area of 4.8 km2, according to the 2021 census statistics. The town's water distribution is facilitated by one ESR, for which the major source of water is from Kotmara dam. Each shapefile layer shows the area covered by one ESR to delineate the various regions on the urban cartography. The determination of ESR's properties and utilization of different features enables a straightforward determination of the spatial position of ESR within a given map of the water supply network, as shown in Table 1.

Table 1

Default ID labels, hydraulics, properties

Labels
Hydraulics
Properties
ObjectID PrefixObjectDefaultsPropertyDefaults
Junctions JU Flow units LPS Node elevation 
Reservoirs RE Head loss formula H-W Tank diameter 50 
Tanks TA Specific gravity Tank height 20 
Pipes PI Relative viscosity Pipe length 50 
Pumps PU Maximum trials 100 Auto length Off 
Valves VA Accuracy 0.001 Pipe diameter 12 
Patterns Balanced Continue Pipe roughness 100 
Curves CU Default pattern   
Labels
Hydraulics
Properties
ObjectID PrefixObjectDefaultsPropertyDefaults
Junctions JU Flow units LPS Node elevation 
Reservoirs RE Head loss formula H-W Tank diameter 50 
Tanks TA Specific gravity Tank height 20 
Pipes PI Relative viscosity Pipe length 50 
Pumps PU Maximum trials 100 Auto length Off 
Valves VA Accuracy 0.001 Pipe diameter 12 
Patterns Balanced Continue Pipe roughness 100 
Curves CU Default pattern   

Table 1 illustrates the settings for a hydraulic modelling software application used in the water distribution system study. It specifies labels and abbreviations for various components such as junctions (JU), reservoirs (RE), tanks (TA), pipelines (PI), pumps (PU), valves (VA), patterns, and curves. Flow units (litres per second (LPS)), head loss formula (H-W, most likely indicating Hazen-Williams), and default values for node elevation, tank diameter, tank height, pipe length, pipe diameter, roughness coefficient, accuracy, maximum trials, and relative viscosity are all specified. These options define the framework for modelling and simulating the behaviour of water distribution networks, assuring reliable and standardized calculations while allowing for customization based on individual network needs. Table 2 illustrates the various tools utilized for incorporating the Junction (Node), Reservoir, Tank, Pipe, Pump, Valve, and Add Label.

Table 2

Junction, pipe, tank properties

Junction IDLongitudeLatitudeElevationBase demandDemand categoriesActual demand
JU1 1,487.83 7,458.3 569 15.7219 15.72 
JU2 1,602.27 7,387.65 568 20.0656 20.07 
TankLongitudeLatitudeElevationInitial levelMinimum levelMaximum level
TA1 1,768.02 7,252.64 568 20 17 20 
TA2 1,792.04 7,155.24 567 22 19 25 
PipeStart nodeEnd nodeLengthDiameterRoughnessLoss coefficient
P12 JU1 JU2 262 100 100 
P23 JU2 JU3 285 150 100 
Junction IDLongitudeLatitudeElevationBase demandDemand categoriesActual demand
JU1 1,487.83 7,458.3 569 15.7219 15.72 
JU2 1,602.27 7,387.65 568 20.0656 20.07 
TankLongitudeLatitudeElevationInitial levelMinimum levelMaximum level
TA1 1,768.02 7,252.64 568 20 17 20 
TA2 1,792.04 7,155.24 567 22 19 25 
PipeStart nodeEnd nodeLengthDiameterRoughnessLoss coefficient
P12 JU1 JU2 262 100 100 
P23 JU2 JU3 285 150 100 

Figure 4 shows the existing water supply network layout, illustrating the interconnection of pipes, nodes, and water sources. Figure 4 presents the water network head at various junctions, providing insights into pressure variations throughout the system. The head values at different junction's aid in identifying areas of potential low pressure or excessive pressure, helping to optimize the network's hydraulic performance. Figure 5 displays the water network flow at various junctions, revealing the distribution of flow rates within the system. By analysing flow patterns and magnitudes, areas of high or low flow can be identified, informing decisions regarding pipe sizing, capacity, and distribution strategies. These figures serve as valuable tools in the evaluation and improvement of the water distribution system in Bota town.
Figure 4

Water network head at various junctions.

Figure 4

Water network head at various junctions.

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Figure 5

Water network flow at various junctions.

Figure 5

Water network flow at various junctions.

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Low velocity in a pipe can be caused by many circumstances. An overly large pipe diameter might make it difficult for the fluid to sustain the necessary velocity. Moreover, changes in the pipe's direction can induce turbulence in the fluid, leading to a decrease in its velocity. Pipe pressure decrease, which can occur from a number of different causes, might be potentially the cause. Low velocity also results from energy losses caused by frictional losses in fluid flow, which are caused by the fluid's contact with the pipe walls. The fluid's viscosity also affects velocity; fluids with greater viscosities need more pressure to maintain the same flow rate as their less viscous equivalents.

Factors generating head loss

Head loss in fluid systems is caused by frictional resistance within pipes, which is affected by material roughness, length, and flow velocity. Bends, elbows, valves, and variations in pipe diameter all contribute to turbulence and increased losses. Entrance and exit impacts, fluid viscosity, elevation variations, pipeline roughness, temperature effects, and compressibility all play important roles. The continuity principle (mass conservation) states that this decrease in area causes the fluid's velocity to rise to maintain a constant flow rate. The change in the volumetric flow due to blockages is explained by Equation (1):
formula
(1)
where B is the volumetric flow rate, Q is the cross-sectional a rea, and V is the velocity of fluid.

Pipe diameter

The flow velocity is directly impacted by a pipe's diameter. If the pipe diameter reduces in a situation where the flow rate is constant, the fluid's velocity rises. On the other hand, the velocity falls as the diameter rises. The concept of continuity, which asserts that for an incompressible fluid in the steady flow, the product of cross-sectional area and velocity remains constant, describes this connection, as shown in Equation (2). Therefore, to maintain a constant flow rate, the velocity varies inversely as the cross-sectional area changes as a result of diameter alterations:
formula
(2)
where and are the cross-sectional areas of the pipe at two different points and and are the corresponding velocities of the fluid at those points.

Pipe pressure

The decrease in pressure occurs as fluid passes through a conduit, which is referred to as a pressure drop. The water's flow rate through the pipe may be impacted by this pressure reduction. The fluid's kinetic energy may rise in response to variables such as friction, obstructions, or changes in pipe diameter, which lowers pressure along the pipe and needs an increase in velocity to maintain the flow rate, as shown in Equation (3). On the other hand, turbulence and system inefficiencies may arise from abnormally high-pressure reductions. For fluid to move efficiently and effectively, the required flow velocity and pressure drop must be balanced:
formula
(3)
where is the pressure drop, g is the friction factor, L is the length of the pipe, Q is the diameter of the pipe, is the density of the fluid, and is the velocity of the fluid.

Frictional loss

Water flowing through a pipe moves through it at a lower velocity due to frictional losses. Friction is created when water flows through a pipe and runs against the pipe walls. A portion of the kinetic energy of the moving water is lost due to friction, which results in heat. As a result of the energy being lost as frictional heat, the water's velocity falls as it moves through the pipe, as shown in Equation (4):
formula
(4)
where is the head loss due to friction, is the friction factor, L is the length of pipe, S is the velocity, and Q is the diameter.

Viscosity of the fluid

Viscosity influences a fluid's resistance to flow, which in turn influences the water's velocity in a pipe. Higher internal friction makes more viscous fluids like honey flow more slowly for a given pressure differential. Water velocity in a pipe tends to drop as viscosity increases and vice versa, as shown in Equation (5):
formula
(5)
where D is hydraulic radius, is pressure difference, Q is the dynamic viscosity, and B represents the length.

Water base demand (LPCD)

The amount of water needed by families for varied everyday requirements and activities is referred to as water demand in the context of residential water supplies. It includes necessities like drinking, cooking, taking a bath, cleaning, and sanitation. Planning and designing water supply systems to provide a dependable and sustainable supply of clean and safe drinking water to communities requires an accurate evaluation of home water demand. Equations (6)–(8) are used to compute and validate the corrected model demand:
formula
(6)
formula
(7)
formula
(8)
where RD indicates required water demand (LPCD), P is the population to be served, CR per capita requirement, and PF is the peak factor. Consider the peak factor as 3 when the population to be served is less than 50,000. is required flow in litre/sec, RD is the required water demand (LPCD), is the required flow in m3/s, and is the required flow in litre/sec.

Leakage loss

As illustrated in Figure 6, a leak valve to each node k represents leakage in the hydraulic model. is the node base demand (consumption), is node pressure, is the leak valve coefficient, and is the leakage flow. The element in EPANET that most closely resembles a leak valve is the emitter, also known as an open valve. This element differs from a regular valve in that it serves as a link between two nodes. Equation (9) is applied to find the leakage:
formula
(9)
where (Emitter coefficient) is 50% (0.5) of the total flow, for bending nodes is 0.6 and for linear node is 0.4, and M is the pressure exponent. The pipe's material determines the value of the pressure exponent. M is 0.5 for metallic pipe, 1.2 or higher for plastic pipe, and close to 1.0 for other materials.
Figure 6

Schematic representation of node leak valve.

Figure 6

Schematic representation of node leak valve.

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The objective of this article is to analyse the domestic water distribution system of Bota town using GIS, RS, and EPANET to deliver potable water to all the areas in the required quantities and under satisfactory hydraulic parameters. The preparation of the water distribution zone map of Bota town water distribution zone map with a morning and evening water distribution schedule with ESR location has been generated using the information listed on them. Various results and graphs indicating flow (LPS), velocity(m/s), unit head loss (m/km), and friction factor in pipes have been identified in the run analysis by running the network in EPANET. The running network in EPANET has additionally showed head (m) and pressure (m) at each junction. The final thematic map of all zones serves as a point of reference for the water distribution zone map of Bota town, shown in Figure 7. The morning and evening water distribution timetables of the Bota town distribution network for each zone's ESR and colours represent two different periods of water distribution in zones. Table 3 depicts a zone map of the morning and evening water distribution schedules. In the layout, the final thematic map of all zones, ESR, and relevant information is prepared.
Table 3

Zonal data

ZoneZone area (km2)House countTap connectionsOperation scheduleOperation time
0.059 123 147 Morning 6:00–8:00 Am 
0.072 200 240 Morning 6:00–8:00 Am 
0.06 328 360 Morning 6:00–8:00 Am 
0.065 31 37 Morning 6:00–8:00 Am 
0.062 94 113 Morning 6:00–8:00 Am 
ZoneZone area (km2)House countTap connectionsOperation scheduleOperation time
0.059 123 147 Morning 6:00–8:00 Am 
0.072 200 240 Morning 6:00–8:00 Am 
0.06 328 360 Morning 6:00–8:00 Am 
0.065 31 37 Morning 6:00–8:00 Am 
0.062 94 113 Morning 6:00–8:00 Am 
Figure 7

Water distribution zone map.

Figure 7

Water distribution zone map.

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The cartographic representation offers a comprehensive depiction of the diverse hydrological distribution regions within the urban centre, demonstrating the partitioning of the locality into discrete zones to facilitate efficient administration and provision of water resources. The implementation of zones aids in the oversight and regulation of water allocation, thereby promoting fair and effective water governance for comprehending the geographical arrangement of water supply infrastructure. It facilitates the decision-making procedures concerning the allocation of water resources and the development of infrastructure in the study area. The bulk coefficient, flow in LPS, velocity in m/s, unit head loss, and friction factor obtained from the pipe of the existing and modified water network are presented in Table 4. Table 4 shows the pipe details or link details, such as flow in LPS and velocity in m/s, of the existing network and the improved network, from which it has been discovered that the existing system had some issues because the velocity in some pipes was less than 0.10 m/s.

Table 4

Results of existing and improved network hydraulic parameter

Existing network
Improved network
Link IDLength (m)Diameter (mm)RoughnessFlow LPSVelocity (m/s)Flow LPSVelocity (m/s)
Pipe PI1 81 500 100 361.21 1.84 407.54 2.09 
Pipe PI2 25 200 44.72 1.42 49.95 1.66 
Pipe PI3 21 36.96 1.18 41.57 1.31 
Pipe PI4 18 28.27 0.90 32.10 1.00 
Pipe PI5 41 4.96 0.16 5.58 0.26 
Pipe PI6 41 5.84 0.19 6.83 0.22 
Pipe PI7 41 6.77 0.22 7.98 0.25 
Pipe PI8 51 18.59 0.59 22.17 0.69 
Pipe PI9 78 12.61 0.40 15.05 0.47 
Pipe PI10 24 44.77 1.43 50.73 1.48 
Pipe PI11 169 500 223.05 1.14 236.91 1.38 
Pipe PI12 60 200 17.6 0.56 21.93 0.53 
Pipe PI13 27 100 14.52 0.46 17.65 1.11 
Pipe PI14 100 2.14 0.04 2.86 0.36 
Pipe PI15 141 250 19.9 0.41 20.52 0.44 
Pipe PI16 51 100 5.66 0.08 7.75 0.76 
Pipe PI17 117 1.19 0.02 1.44 0.21 
Pipe PI18 119 4.67 0.09 6.94 0.54 
Pipe PI19 74 2.58 0.05 2.72 0.80 
Pipe PI20 111 250 22.7 0.09 30.57 0.75 
Pipe PI21 155 100 3.64 0.02 4.32 0.61 
Pipe PI22 155 250 15.8 0.32 23.05 0.40 
Pipe PI23 33 500 177.36 0.90 242.29 1.08 
Pipe PI24 42 250 64.81 1.32 90.50 1.61 
Pipe PI25 54 33.86 0.69 46.05 0.83 
Pipe PI26 24 250 32.75 0.67 41.11 0.79 
Pipe PI27 41 19.89 0.41 25.25 0.48 
Pipe PI28 86 150 5.81 0.08 7.28 0.46 
Pipe PI29 239 250 10.16 0.21 13.37 0.26 
Pipe PI30 156 150 6.67 0.09 8.51 0.54 
Pipe PI31 160 250 18 0.37 24.62 0.50 
Pipe PI32 148 13.21 0.27 17.84 0.36 
Pipe PI33 56 500 105.47 0.54 126.53 0.68 
Pipe PI34 82 102.4 0.52 123.19 0.66 
Pipe PI35 79 250 35.85 0.73 43.65 0.94 
Pipe PI36 75 32.24 0.66 39.64 0.86 
Pipe PI37 266 500 46.07 0.23 54.52 0.29 
Pipe PI38 24 250 10.55 0.21 12.56 0.27 
Pipe PI39 80 7.62 0.16 9.34 0.21 
Pipe PI40 151 25.08 0.51 29.30 0.64 
Pipe PI41 22 15.45 0.31 17.68 0.38 
Pipe PI42 77 10.43 0.21 12.32 0.26 
Pipe PI43 61 100 5.02 0.08 6.06 0.96 
Pipe PI44 596 250 15.72 0.09 20.50 0.53 
Pipe PI45 85 500 261.93 1.33 324.02 1.70 
Pipe PI46 34 42.63 0.22 50.31 0.25 
Pipe PI47 111 250 26.38 0.09 30.87 0.72 
Pipe PI48 28 15.58 0.32 17.79 0.36 
Pipe PI49 110 100 8.12 0.08 9.30 1.35 
Pipe PI50 184 100 5.27 0.08 6.76 1.01 
Pipe PI51 173 3.33 0.07 4.00 0.58 
Pipe PI52 153 250 8.15 0.17 11.02 0.24 
Pipe PI53 92 500 313.72 1.60 354.81 2.02 
Pipe PI54 93 250 10.12 0.21 12.74 0.25 
Pipe PI55 169 100 6.13 0.09 7.89 1.20 
Existing network
Improved network
Link IDLength (m)Diameter (mm)RoughnessFlow LPSVelocity (m/s)Flow LPSVelocity (m/s)
Pipe PI1 81 500 100 361.21 1.84 407.54 2.09 
Pipe PI2 25 200 44.72 1.42 49.95 1.66 
Pipe PI3 21 36.96 1.18 41.57 1.31 
Pipe PI4 18 28.27 0.90 32.10 1.00 
Pipe PI5 41 4.96 0.16 5.58 0.26 
Pipe PI6 41 5.84 0.19 6.83 0.22 
Pipe PI7 41 6.77 0.22 7.98 0.25 
Pipe PI8 51 18.59 0.59 22.17 0.69 
Pipe PI9 78 12.61 0.40 15.05 0.47 
Pipe PI10 24 44.77 1.43 50.73 1.48 
Pipe PI11 169 500 223.05 1.14 236.91 1.38 
Pipe PI12 60 200 17.6 0.56 21.93 0.53 
Pipe PI13 27 100 14.52 0.46 17.65 1.11 
Pipe PI14 100 2.14 0.04 2.86 0.36 
Pipe PI15 141 250 19.9 0.41 20.52 0.44 
Pipe PI16 51 100 5.66 0.08 7.75 0.76 
Pipe PI17 117 1.19 0.02 1.44 0.21 
Pipe PI18 119 4.67 0.09 6.94 0.54 
Pipe PI19 74 2.58 0.05 2.72 0.80 
Pipe PI20 111 250 22.7 0.09 30.57 0.75 
Pipe PI21 155 100 3.64 0.02 4.32 0.61 
Pipe PI22 155 250 15.8 0.32 23.05 0.40 
Pipe PI23 33 500 177.36 0.90 242.29 1.08 
Pipe PI24 42 250 64.81 1.32 90.50 1.61 
Pipe PI25 54 33.86 0.69 46.05 0.83 
Pipe PI26 24 250 32.75 0.67 41.11 0.79 
Pipe PI27 41 19.89 0.41 25.25 0.48 
Pipe PI28 86 150 5.81 0.08 7.28 0.46 
Pipe PI29 239 250 10.16 0.21 13.37 0.26 
Pipe PI30 156 150 6.67 0.09 8.51 0.54 
Pipe PI31 160 250 18 0.37 24.62 0.50 
Pipe PI32 148 13.21 0.27 17.84 0.36 
Pipe PI33 56 500 105.47 0.54 126.53 0.68 
Pipe PI34 82 102.4 0.52 123.19 0.66 
Pipe PI35 79 250 35.85 0.73 43.65 0.94 
Pipe PI36 75 32.24 0.66 39.64 0.86 
Pipe PI37 266 500 46.07 0.23 54.52 0.29 
Pipe PI38 24 250 10.55 0.21 12.56 0.27 
Pipe PI39 80 7.62 0.16 9.34 0.21 
Pipe PI40 151 25.08 0.51 29.30 0.64 
Pipe PI41 22 15.45 0.31 17.68 0.38 
Pipe PI42 77 10.43 0.21 12.32 0.26 
Pipe PI43 61 100 5.02 0.08 6.06 0.96 
Pipe PI44 596 250 15.72 0.09 20.50 0.53 
Pipe PI45 85 500 261.93 1.33 324.02 1.70 
Pipe PI46 34 42.63 0.22 50.31 0.25 
Pipe PI47 111 250 26.38 0.09 30.87 0.72 
Pipe PI48 28 15.58 0.32 17.79 0.36 
Pipe PI49 110 100 8.12 0.08 9.30 1.35 
Pipe PI50 184 100 5.27 0.08 6.76 1.01 
Pipe PI51 173 3.33 0.07 4.00 0.58 
Pipe PI52 153 250 8.15 0.17 11.02 0.24 
Pipe PI53 92 500 313.72 1.60 354.81 2.02 
Pipe PI54 93 250 10.12 0.21 12.74 0.25 
Pipe PI55 169 100 6.13 0.09 7.89 1.20 

Figure 8 displays the hydraulic model's calibration by comparing simulated and observed pressure and flow, with a focus on the first and second iterations. In the first iteration (Figure 8(a)), the simulated results are presented alongside the observed data for pressure and flow. Discrepancies between model projections and real-world measurements have been found, indicating the need for changes. These differences can be related to errors in input parameters and system features that influence the hydraulic behaviour. Following the first iteration, the model is adjusted depending on the identified disparities. The revised model is then exposed to a second iteration (Figure 8(b)), during which a new comparison of simulated and observed pressure and flow is made. This repeated procedure continues until a suitable level of agreement is attained, indicating that the hydraulic model has been effectively calibrated.
Figure 8

Calibration of simulated and observed pressure and flow: (a) first iteration and (b) second iteration.

Figure 8

Calibration of simulated and observed pressure and flow: (a) first iteration and (b) second iteration.

Close modal
The velocities in pipes numbered PI20, PI21, PI22, PI26, and PI33 are less than 0.10 m/s. The values for PI20, PI21, PI22, PI26, and PI33 are all less than 0.10 m/s. The pipes with a violet colour indicate that the velocity is less than 0.10 m/s, as shown in Figure 9. The flow velocity of the network is insufficient. Modifications are made to the current network design by altering the diameter of certain pipes, namely, PI13, PI14, PI16, PI17, PI18, PI19, PI20, PI21, PI28, PI30, PI43, PI44, PI47, PI49, PI50, PI51, and PI55, as shown in Table 5. These adjustments resulted in an increase in the velocity of the pipes, specifically PI20, PI21, PI22, PI26, and PI33, surpassing the threshold of 0.10 m/s, as shown in Figure 10. A minimum flow velocity of 0.10 m/s has been maintained. The findings of the flow and velocity are compared between the present network and the revised network architecture in Figure 11.
Table 5

Modified diameter of hydraulic model

Link IDDiameter of existing network (mm)Diameter of improved network (mm)
Pipe PI13 200 100 
Pipe PI14 250 
Pipe PI16 
Pipe PI17 
Pipe PI18 
Pipe PI19 
Pipe PI20 500 250 
Pipe PI21 100 
Pipe PI28 250 150 
Pipe PI30 
Pipe PI43 100 
Pipe PI44 500 250 
Pipe PI47 
Pipe PI49 250 100 
Pipe PI50 
Pipe PI51 
Pipe PI55 250 100 
Link IDDiameter of existing network (mm)Diameter of improved network (mm)
Pipe PI13 200 100 
Pipe PI14 250 
Pipe PI16 
Pipe PI17 
Pipe PI18 
Pipe PI19 
Pipe PI20 500 250 
Pipe PI21 100 
Pipe PI28 250 150 
Pipe PI30 
Pipe PI43 100 
Pipe PI44 500 250 
Pipe PI47 
Pipe PI49 250 100 
Pipe PI50 
Pipe PI51 
Pipe PI55 250 100 
Table 6

Results of existing and improved network's base demand with municipal record

JunctionBase demand LPS as per municipal bodyBase demand LPS as per existing networkActual demand (including leakage) LPSBase demand LPS as per improved networkActual demand (including leakage) LPS
0.00 0.00 2.33 0.00 2.76 
2.37 2.80 
2.42 2.85 
2.47 2.90 
3.09 2.55 4.94 5.82 7.50 
3.94 3.14 5.70 4.12 5.84 
4.84 3.56 6.50 5.02 6.77 
4.05 3.22 5.83 4.23 5.98 
3.66 2.77 5.65 3.84 6.72 
10 6.13 4.96 7.67 6.31 9.17 
11 3.15 2.39 5.14 3.33 6.12 
12 0.00 0.00 2.26 0.00 2.64 
13 1.41 0.71 3.48 1.57 4.11 
14 0.00 0.00 2.42 0.00 2.69 
15 3.38 2.77 5.23 3.56 5.10 
16 1.52 0.84 3.57 1.68 3.29 
17 4.16 2.76 6.10 4.34 7.27 
18 9.84 7.55 10.93 10.02 13.06 
19 3.49 2.82 5.68 3.67 5.65 
20 4.05 3.22 6.10 4.21 7.21 
21 13.28 10.88 13.79 13.46 16.46 
22 1.13 0.47 3.62 1.29 4.28 
23 4.50 3.68 6.48 4.68 7.68 
24 6.08 4.80 7.84 6.26 9.33 
25 3.83 1.98 5.16 3.99 7.08 
26 13.67 11.69 14.91 13.83 15.80 
27 14.46 11.55 14.85 14.64 17.74 
28 11.03 8.95 11.98 11.21 13.21 
29 11.98 9.39 12.82 12.14 15.36 
30 15.75 12.65 16.00 15.96 18.00 
31 2.76 2.04 5.22 2.92 6.18 
32 4.39 3.56 6.58 4.55 6.66 
33 1.63 0.98 4.28 1.81 3.91 
34 7.65 5.79 9.58 7.83 10.16 
35 3.49 2.79 5.87 3.65 5.80 
36 0.90 0.33 3.47 1.08 3.07 
37 1.46 0.48 4.03 1.64 4.76 
38 1.35 0.53 4.02 1.53 3.61 
39 7.43 5.92 8.76 7.61 10.44 
40 0.96 0.19 3.32 1.14 2.92 
41 5.74 4.37 7.21 5.90 7.62 
42 6.64 5.21 8.11 6.82 9.63 
43 2.03 1.42 4.26 2.21 5.02 
44 2.42 1.69 4.59 2.60 5.41 
45 2.98 2.03 5.11 3.16 4.99 
46 13.67 10.69 14.02 13.85 15.70 
47 1.29 0.49 4.06 1.45 3.61 
48 1.97 0.86 4.21 2.15 3.99 
49 3.99 3.22 6.03 4.17 6.05 
50 6.53 4.96 7.51 6.69 8.14 
JunctionBase demand LPS as per municipal bodyBase demand LPS as per existing networkActual demand (including leakage) LPSBase demand LPS as per improved networkActual demand (including leakage) LPS
0.00 0.00 2.33 0.00 2.76 
2.37 2.80 
2.42 2.85 
2.47 2.90 
3.09 2.55 4.94 5.82 7.50 
3.94 3.14 5.70 4.12 5.84 
4.84 3.56 6.50 5.02 6.77 
4.05 3.22 5.83 4.23 5.98 
3.66 2.77 5.65 3.84 6.72 
10 6.13 4.96 7.67 6.31 9.17 
11 3.15 2.39 5.14 3.33 6.12 
12 0.00 0.00 2.26 0.00 2.64 
13 1.41 0.71 3.48 1.57 4.11 
14 0.00 0.00 2.42 0.00 2.69 
15 3.38 2.77 5.23 3.56 5.10 
16 1.52 0.84 3.57 1.68 3.29 
17 4.16 2.76 6.10 4.34 7.27 
18 9.84 7.55 10.93 10.02 13.06 
19 3.49 2.82 5.68 3.67 5.65 
20 4.05 3.22 6.10 4.21 7.21 
21 13.28 10.88 13.79 13.46 16.46 
22 1.13 0.47 3.62 1.29 4.28 
23 4.50 3.68 6.48 4.68 7.68 
24 6.08 4.80 7.84 6.26 9.33 
25 3.83 1.98 5.16 3.99 7.08 
26 13.67 11.69 14.91 13.83 15.80 
27 14.46 11.55 14.85 14.64 17.74 
28 11.03 8.95 11.98 11.21 13.21 
29 11.98 9.39 12.82 12.14 15.36 
30 15.75 12.65 16.00 15.96 18.00 
31 2.76 2.04 5.22 2.92 6.18 
32 4.39 3.56 6.58 4.55 6.66 
33 1.63 0.98 4.28 1.81 3.91 
34 7.65 5.79 9.58 7.83 10.16 
35 3.49 2.79 5.87 3.65 5.80 
36 0.90 0.33 3.47 1.08 3.07 
37 1.46 0.48 4.03 1.64 4.76 
38 1.35 0.53 4.02 1.53 3.61 
39 7.43 5.92 8.76 7.61 10.44 
40 0.96 0.19 3.32 1.14 2.92 
41 5.74 4.37 7.21 5.90 7.62 
42 6.64 5.21 8.11 6.82 9.63 
43 2.03 1.42 4.26 2.21 5.02 
44 2.42 1.69 4.59 2.60 5.41 
45 2.98 2.03 5.11 3.16 4.99 
46 13.67 10.69 14.02 13.85 15.70 
47 1.29 0.49 4.06 1.45 3.61 
48 1.97 0.86 4.21 2.15 3.99 
49 3.99 3.22 6.03 4.17 6.05 
50 6.53 4.96 7.51 6.69 8.14 
Figure 9

Water network velocity in existing system.

Figure 9

Water network velocity in existing system.

Close modal
Figure 10

Water network velocity in improved system.

Figure 10

Water network velocity in improved system.

Close modal
Figure 11

Comparison of (a) flow, (b) velocity, and (c) pressure in existing network and improved network.

Figure 11

Comparison of (a) flow, (b) velocity, and (c) pressure in existing network and improved network.

Close modal

Table 6 shows that the basic demand cannot be satisfied by the existing network. The ‘Base Demand LPS as Per Improved Network’ is noticeably higher than the demand ascertained by the municipal body and the existing network, indicating that the enhanced network is more capable of providing the necessary water. When the network is improved, demand at several junctions rises significantly, notably Junctions 5, 6, 7, 8, 10, 15, 17, 18, 21, 24, 26, 27, 28, 29, 30, 34, 39, 41, 42, 43, 45, and 46. This shows that certain regions have benefitted from the improved network. With a demand of 15.96 LPS in the better network scenario, Junction 30 stands out as the most important intersection, highlighting the advantages of the improved network at this junction. In addition, compared to the municipal body's estimations, the improved network shows more constant and uniform demand levels across different junctions. It is discovered that the existing network is inadequate in comparison to the estimations made by the municipal authority. All things considered, the results point to an improved network's greater ability to deliver water when needed.

According to the findings of the simulation, the water distribution system had low pressure in certain parts of the network and excessive head loss in other parts of the network. The investigation also uncovered the presence of several water leaks inside the network, which is one of the contributing factors to the loss of water. Based on the findings, many alterations have been suggested as potential ways to enhance the performance of the water distribution system. The suggested changes included the installation of booster pumps to enhance the pressure in regions with low pressure, the replacement of old pipes with new pipes to decrease head loss, and the repair of leakages to reduce the amount of water that was lost.

Researchers working in water distribution systems have performed work that is analogous to this work. GIS technology has been implemented for conducting spatial analysis and EPANET software for conducting hydraulic modelling and optimization (Sivakumar et al. 2016; Seyoum & Tanyimboh 2017). The researchers wanted to enhance the design of the water supply network by optimising the pipe sizes, flow rates, and pressures while considering a variety of restrictions and design goals. These studies highlight how important it is to use GIS, RS, and EPANET in the process of analysing and designing water distribution systems. Researchers can increase the efficacy, sustainability, and dependability of water supply networks by making use of the technology and software tools, which in turn leads to improvements in water management procedures (Farina et al. 2014; Georgescu et al. 2014; Venkata Ramana et al. 2015).

This method of utilising GIS, RS, and EPANET offers time-saving advantages, but it also has some limitations. One of the most significant limitations is the need for in-depth software knowledge. Implementation and interpretation of the results are highly dependent on the researchers' skill with software applications. A lack of knowledge or familiarity with the software may compromise the precision and efficacy of the analysis. In addition, the efficacy of the procedure affected the used computer system. The computer system may not efficiently support or respond to certain tasks, potentially affecting the outcome and overall analysis process. In terms of anticipated outcomes, the study intends to produce a map of water distribution zones and conduct an inspection and analysis of the distribution system to ensure the equitable distribution of the requisite water quantity to each location. The authors propose combining GIS, RS, and EPANET to gain a more comprehensive comprehension of the water distribution network. These technologies can facilitate the design of an effective and sustainable water distribution system by providing valuable insights into the system's dynamics.

The current study used GIS, RS, and EPANET with ground data to analyse and develop Bota town's water distribution infrastructure. Calibration of a hydraulic model is an important step in ensuring the accuracy and reliability of its predictions. This procedure entails comparing the model's computed outputs to real-world pressures and flows, which allows for modifications and revisions to improve the model's performance. The calibration process is iterative, including numerous rounds of modifications to obtain ideal alignment between simulated and measured data. According to the simulation results, the Bota town's water distribution system has various issues, including low pressure, high head loss, and leakages. The performance of the water distribution system is expected to improve as a result of planned network enhancements, allowing for the continuous provision of safe and clean drinking water to Bota's growing population. The research approach has the potential to be utilised by other urban regions suffering identical water distribution challenges to improve their water delivery systems. While this research provides significant insights, there are several limits that must be acknowledged. First, the accuracy of the hydraulic modelling process is dependent on the quality and availability of data. Incomplete or erroneous water distribution network data, including pipe parameters, demand patterns, and operational variables, might inject uncertainties and potential mistakes into the results. This emphasizes the need to invest in data gathering and quality assurance processes to ensure the integrity of the dataset. The simplifications and assumptions used in hydraulic modelling must be viewed as a constraint. These simplifications are frequently required to make calculations more manageable, but they may not fully capture the complexities of real-world systems. Assuming steady-state settings and ignoring transient effects, for example, may limit the model's ability to capture the network's dynamic behaviour. Furthermore, the success of hydraulic modelling is dependent on the software capabilities and processing resources available. Limited processing capacity or access to advanced modelling tools may limit the analysis's complexity and scale. Researchers should attempt to incorporate transitory behaviours, pressure surges, and dynamic demand patterns into their assessments to create more realistic modelling. This method will provide for a more in-depth and accurate assessment of the system's performance under varied operational settings. Furthermore, it is necessary to widen the scope of the research to include water quality issues, addressing the influence of water quality metrics and contamination threats inside the distribution system.

The approach for applying GIS in water distribution networks cuts down on the amount of time needed to collect and store data in such networks, hence reducing the amount of time necessary for those tasks. The interchange of data between the hydraulic analysis models hosted on EPANET and the GIS system contributes to the improvement of engineering design and analysis. In the future, studies on the topic should go beyond hydraulic analysis to include factors of sustainability and resilience, such as how the network can adjust to environmental changes caused by climate change. Finally, multidisciplinary collaboration among water engineers, environmental scientists, and data analysts can contribute a variety of viewpoints to the research, fostering novel solutions and addressing the complex difficulties in water distribution network management. These proposals will serve as a guideline for future studies aimed at improving the robustness, sustainability, and resilience of water supply networks.

The corresponding author takes responsibility on behalf of all authors for ethical approval and permissions related to this research work.

The corresponding author is responsible for getting participation consent on behalf of all authors.

All the parties gave their written permission for the article to be published. The corresponding author takes responsibility on behalf of all authors for consent to publish.

All authors added to the idea and planning of the study. Pranit Nitin Dongare put together the materials, helped with making graphs and supervised the whole project and gathered the data. The work on GIS and EPANET was done by Dr Kul Vaibhav Sharma and Pranit Nitin Dongare. The text was written by Dr Vijendra Kumar. Dr Aneesh Mathew did the work of analysing. All authors have read the final draught and agreed with it.

The corresponding author, on behalf of all authors, certifies that no funding or grants were received during preparation.

The datasets generated during the current study are available from the corresponding author on reasonable prior request.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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