India's flood risk assessment and mapping with multi-criteria decision analysis and GIS integration

Flood risk assessment is a critical aspect of catastrophe management and resilience that usually involves several spatial and analytic techniques. Being a big, diverse country, India has suffered terrible floods before (Dhiman et al. 2019; Kedam et al. 2024). Climate change, rapid urbanization, land-use planning deficiency, and other factors exacerbate these natural disasters (Zhao et al. 2024). Together these factors amount to flood hazard assessment and mitigation (Aerts et al. 2018). The use of the geographic information system (GIS) and multi-criteria decision analysis (MCDA) technologies helps provide comprehensive data on flooding risks and can serve as a solid base for detailed flood risk maps throughout the world (Elkhrachy et al. 2023; Wei et al. 2023). Flooding in India comes with a wide scale of effects including a great number of casualties, damage to crucial services, and disturbance of livelihoods and communities (Mukherjee et al. 2023). Integration of GIS and MCDA, however critical, should be recognized considering the catastrophic effects of such disasters (Solanki et al. 2024). An all-embracing strategy involving these components would enable decision makers to identify vulnerability points and prepare mitigation strategies early to reduce risk exposure and consequently make India more resistant to such flood disasters (Afsari et al. 2022; Kumar 2024).

Many of these studies point to evidence that GIS, remote sensing, and other methodical tools are essential in identifying flood risk and vulnerability. According to Radwan et al. (2019), GIS and remote sensing techniques (RST) are instrumental in dry land risk assessment. Therefore, this proves why digital elevation models (DEMs), soil maps, and historical rainfall data should be utilized in various Regional Development Plans (RDPs). The findings indicate that RST and GIS will be effective tools in reducing flood risk as well as minimizing runoff and rainwater harvesting. Using GIS remotely sensed data, Saha & Agrawal 2020 have worked towards pre-flood assessment for the detection of prior floods and land use and land cover (LULC) classification analysis. Ghosh & Kar (2018) studied the flood risk in the lowest Gangetic basin, and particularly the Malda division of West Bengal. Compound hazard and vulnerability indices were used in conjunction with the analytical hierarchy process (AHP) to obtain a flooding risk map. In addition, the study shows that flood vulnerability is greatest in the north and west component of the district. Using the MCDA and AHP, Gupta & Dixit (2022) examine the flood risks in flood-prone areas of Assam, India. This includes several types of factors, namely, land use, meteorology, geology, and topography. The analysis of the results illustrates that the majority of the regions experience moderate to very high flood risk levels.

Tomar et al. (2021) propose a holistic system involving remote sensing, GIS, and field survey techniques for identifying flood-prone urban districts. This paper presents an innovative approach to the problem of an inefficient urban drainage system in the Yamuna River National Capital Territory of Delhi, India. This provides guidance for preparedness in anticipation of future flooding incidents and improves the understanding of what gives rise to more than the usual incidences of flooding. Matheswaran et al. (2019) extend their methods in determining flood risk in the South Asian region through clustering algorithms and multi-temporal remote sensing data for identifying flood centres or hot spots. This research also identifies potential pilot zones for flood index insurance products. However, a recent study by Ha et al. (2023) can provide insight into the analysis of flood risk in the Quang Binh province of Vietnam using the integrated machine learning (ML)-AHP framework. However, AHP plays a role in incorporating vulnerability factors, while ML models are used to make the flood hazard map. An outcome is a detailed flood risk map pinpointing variation zones. Hagos et al. (2022) focus on the Teji watershed, a rain-fed area that is prone to flooding. The research looks at data from soil, slope, elevation, drainage density (DD), and land use to estimate the flood risk. Such results feed into early warning mechanisms and flood prediction with respect to highly endangered and catastrophic zones for riverine floods.

Skakun et al. (2014) describe a novel approach to mapping flooding risks and hazards using time-series satellite images. This is an effective way of assessing flood risks when there are many uncertainties coupled with data deficiency. The above research demonstrates its use in Namibia's Katima Mulilo region. The study of Ahmed et al. (2022), conversely, presents a multi-criteria approach to mapping flood risk that considers mitigating capacities. As such, the research presents a holistic analysis of flood risk that incorporates several elements, such as hazards, susceptibilities, and mitigations. It seems that mitigation measures can be incorporated into flood risk management. Vojinovic et al. (2016) outlined a comprehensive methodology of flood risk assessment considering both quantitative and subjective factors. In other words, this method could also be used together with the conventional hazard assessment indicators as well as the community perception of risk. Baky et al. (2020) propose a different approach that encompasses the use of flood hazard and vulnerability index for flood risk assessment. Accordingly, the issue of risks implies paying attention to risk and its susceptibility in the assessment process. This also emphasizes the need for the use of GIS remote sensing as well as many other analytical tools in flood risk assessment.

India is a vast country that encompasses different ecologies ranging from sub-Himalayas to coastal lands, making flood management very challenging (Roy et al. 2021). Thus, monsoon, cyclone, and river morphology generate unique flood risk maps consisting of different areas across the whole territory. Therefore, the use of sophisticated geospatial technologies such as GIS with MCDA's ability to process decisions becomes crucial in understanding and handling the multiple causes leading to flood risks in this big country (Levy et al. 2007). This study thoroughly examines how integrating GIS and MCDA can be applied in flood hazard mapping and evaluation in India. Data collection is the first step in the process and involves gathering a variety of data. Soil types, land-use classification, topographical characteristics, rainfall patterns, river-flow data, and a myriad of meteorological and hydrology indicators are included in the list. For a good foundation towards flood hazard assessment, such a large amount of data is important. The next step in GIS is integrating various datasets into one coherent structure of information at one geographical point. With GIS technology spatial layers that contain data about elevation models, hydrologic features, land use, and other important data become possible (Zhou et al. 2023). They form an essential basis for subsequent study. Hazard modelling in GIS involves the use of mathematical algorithms that are employed in determining possible flood occurrences. These models comprise some parameters that assist in determining locations expected to be hit by floods such as rainfall intensity, geography, and river flows. This leads to the creation of a flood risk map indicating the susceptible spots. Vulnerability assessment represents the subsequent important step, and through MCDA analyses the susceptibility of different elements is exposed. This also includes infrastructure, ecosystems, and natural cultures besides humans. Susceptibility can also be with respect to population density, building materials, and proximity to essential facilities (Karymbalis et al. 2021; Yin et al. 2023).

In this process, each of the criteria in MCDA is assigned a score according to its importance. For instance, while reviewing the vulnerability of a location, population density could be weighed higher than transport infrastructure. The objective of developing a comprehensive vulnerability map is to show the specific areas and resources at risk of flooding. The basic element in this technique is overlay analysis that combines the GIS with MCDA to add the hazard-vulnerability layers (Nyimbili et al. 2018; Chen 2022). Through this procedure, hazardous areas together with vulnerable areas are grouped to form risky zones. In these locations, flood is expected to hit with the most intensity. These efforts build up to a risk assessment that determines total flood risk in different areas by integrating findings from hazard and vulnerability studies. This is risk assessment, upon which making informed decisions and prioritizing approaches for risk management can rely. The results produce the flood hazard mapping, which shows different levels of flood risk in the study area. These maps are essential for planning land use, emergency responses, and disaster preparation. This general approach has quite an impact. Using GIS and MCDA to determine high-risk zones helps prioritize adaptation versus mitigation for improving resilience to recurrent flood events in India. Additionally, sharing the flooding risk map is key to increasing public awareness and promoting community preparedness. Providing actionable information helps mitigate catastrophes and creates a sense of collective responsibility in tackling communal challenges.

The objectives of this study are to locate areas vulnerable to floods across India, estimate flooding threats, evaluate the susceptibility of communities and facilities to destruction by water, and rank precautions worth undertaking against flood risks. Supporting sustainable development, raising awareness, and developing the capacity to mitigate risks are the demonstrations of the applications of the GIS–MCDA approach. These objectives hope to increase India's disaster preparedness and resilience and thereby lessen the effects of flooding on people and property.

India is truly a special place that is blessed with different kinds of topographies such as rivers, mountains, valleys, plateaus, seas, deserts, and plains. India is situated towards the northern part of the Equator. It has an outstanding coastline that stretches for 7,517 km with a coastal perimeter of 5,423 km along the Peninsular Indian region and 2,094 km across the Andaman, Nicobar, and Lakshadweep Island chain. India is ranked the 7th largest nation in the world and has an area of 3,287,263 km². In turn, this varied landscape spreads out into different areas, namely, plains, highlands, the southern peninsula, and deserts. The Himalayan Mountains embrace the northern part of India. India is made up of four huge regions, each exhibiting distinct natural features. The eastern and central part of the country, known as the Indo-Gangetic plain, dominates with its products and nutrients. Southwards, India is characterized by the prominent coastal ranges of the Western Ghats and the Eastern Ghats, each making a strong topographical statement. Also, the Aravalli and Vindhyachal Mountain Ranges make this combination of India's striking geographical features even more fascinating.

India is bordered by countries such as Afghanistan, Pakistan, Nepal, Bhutan, China, Myanmar, and Bangladesh. The international borders help enhance India's cultural and physical diversities, which makes for a distinct national image. However, amidst this scenic tapestry, India faces a significant challenge: flooding. Approximately 12.5% of the country's land is prone to flooding, making it the second most flood-affected nation globally, after Bangladesh. The flood season spans about four months, from June to the end of September, contributing nearly three-quarters of India's annual rainfall. Floods affect approximately 40 million hectares annually, with damages impacting over 3.6 million hectares of cultivated land on average.

India's topography ranges from the Himalayan Mountain range in the north to coastal plains in the south, with plateaus, deserts, and river valleys in between. Each of these regions has unique characteristics that influence climate, agriculture, population distribution, and development. India experiences a wide range of climatic conditions, from tropical in the south to temperate and alpine in the north. This variability affects agriculture, water resources, and habitation patterns. Human development indicators such as literacy, income, health, and infrastructure vary widely across states and even within states. Urban areas typically have higher development indices compared to rural areas.

This section outlines the parameters used for flood susceptibility analysis: elevation, slope, drainage density, distance to roads, distance to rivers, rainfall, and land use/land cover. Each parameter is crucial in determining flood risk:

• (a) Elevation: Lower elevations are more flood prone.

• (b) Slope: Flat areas retain water longer, increasing flood risk.

• (c) Drainage Density (DD): Higher DD leads to more surface runoff and greater flood risk.

• (d) Distance to Roads: Roads can block water flow, heightening nearby flood risks.

• (e) Distance to Rivers: Areas closer to rivers are more vulnerable to flooding.

• (f) Rainfall: Higher rainfall contributes to increased runoff and flooding.

• (g) Land Use/Land Cover (LULC): Impervious urban surfaces amplify runoff, while vegetation reduces it.

The following list provides data sources and methods used to quantify these parameters for GIS-based flood risk mapping.

• (a) Elevation ASTER GDEM Version 3, 30 × 30 m https://search.earthdata.nasa.gov

• (b) Slope ASTER GDEM Version 3, 30 × 30 m https://search.earthdata.nasa.gov

• (c) DD ASTER GDEM Version 3, 30 × 30 m https://search.earthdata.nasa.gov

• (d) Distance to Road Vector layer Geological Survey of India (GSI) data, https://www.gsi.gov.in

• (e) Distance to Rivers Vector layer GSI data, https://www.gsi.gov.in

• (f) Rainfall high-resolution gridded data, https://chrsdata.eng.uci.edu/

• (g) Land-use Landsat 8 OLI/TIRS USGS, 30 × 30 m https://earthexplorer.usgs.gov

The integration of GIS with MCDA represents a valuable approach for mapping flood risk and hazard zones within river basins. This strategy is very useful for designing flood control plans in remote communities and estimating the risk of populations living in these locations. The major goal of this research is to assess the spatial extent of flood danger zones in India and to identify communities at high risk during flood occurrences. For example, the MCDA approach is applied to define flood-prone areas, while the GIS overlay studies identify the settlements susceptible to flooding. The methodology considers seven essential spatial parameters: distance from rivers, proximity to the roads, LULC, slope, elevation, DD, and annual average rainfall. These findings identify such risky locations prone to flood hazards, providing significant data for policymaking, scientific research, and local administration to act in time and reduce overall losses.

The most important part of this research work was making the zonation map based upon several significant factors. Satellite imagery formed the primary data source. However, some supplementary data came from different maps issued by various government offices and departments. The data were subjected to extensive pre-processing before analysis. Later, these pre-prepared data layers were carefully included in a geo-database with respect to the GCS_WGS_1983 reference system. LULC maps were key data elements that were meticulously extracted from high-quality satellite images. The National Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) image provided a precise contour map marked out every 20 m for producing DEMs and slope maps. The rainfall distribution map for the specific region is based on Indian Meteorological Department data and this process is called inverse distance weighted interpolation.

The selection of flood susceptibility parameters in this study is grounded in a comprehensive analysis of factors that significantly influence flood risk, supported by empirical evidence, literature reviews, and expert consultations. Topographical features such as elevation and slope are critical as they determine water flow and accumulation, with low-lying and flat areas being more prone to prolonged inundation. Climate factors, particularly average annual rainfall, are primary drivers of flooding, influencing the volume of surface runoff. Soil characteristics, represented by DD, help in understanding how quickly water is transported away from an area, impacting flood susceptibility. LULC data are essential to assess the influence of different land covers on water absorption and runoff, where urban areas typically exhibit higher runoff due to impervious surfaces. Additionally, infrastructure and socioeconomic factors, such as proximity to roads and rivers, are crucial as they can obstruct natural water flow, increase runoff, and indicate areas more susceptible to flooding due to river overflows. These parameters were integrated using GIS and MCDA to ensure a holistic and accurate flood susceptibility assessment, encompassing physical, climatic, and anthropogenic factors that contribute to flood risk.

For the analysis, ranking and weights were assigned to all the seven criteria used during flood risk vulnerability mapping in this study. The geospatial environment had been defined risks ranked 1–5, which served as the standard in classifying each criterion. After ranking all layers, the total weight of 100 that is spread proportionately across the seven parameters is then employed for vulnerability assessment based on priority. Subsequently, the normalized weight index (NWI) values were calculated using the raster tool. The NWI was determined by dividing the total weight by each individual criterion's weight. The integration of the normalized rank index and the NWI was performed using a weighted sum overlay analysis within the spatial analysis. Based on their relative impact on flood risk, which is ascertained using a combination of expert opinion, empirical data analysis, and statistical methods, parameters in the flood susceptibility evaluation are given weights. Empirical data analysis looks at past flood data to find trends and connections between different variables and the frequency of floods.

Table 1 presents the breakdown of the criteria used in a flood risk vulnerability assessment, each accompanied by its assigned weight and the corresponding NWI value. These values are crucial for understanding the relative importance of each criterion in assessing flood vulnerability in the study area. ‘Rainfall’ is the most influential factor, carrying a 25% weight and an NWI value of 0.25, signifying its substantial impact on flood vulnerability. ‘Distance to River’ is also significant with a 20% weight and an NWI value of 0.20, indicating its importance in flood risk assessment. ‘Drainage Density’ and ‘Elevation’ are both vital, each with a 15% weight and an NWI value of 0.15, showing their substantial influence on vulnerability. ‘Land Use/Land Cover’ and ‘Slope’ are moderately important, each with a 10% weight and an NWI value of 0.10. ‘Distance to Road’ is the least influential criterion with a 5% weight and an NWI value of 0.05.

Table 3 presents an extensive analysis of the several elements that affect the study area's vulnerability to flooding. Each component is classified into distinct groups and given a corresponding Risk Rank. These variables include important components that are crucial in identifying an area's susceptibility to floods. There are many ranges for the attribute ‘Elevation’ ranging from very low to very high. It is notable that most of the region (58.21%) is made up of places with ‘Very Low’ elevation. Because low-lying locations are usually more vulnerable to flooding during flood occurrences, elevation is an important consideration when determining flood risk. The terrain's ‘Slope’ is then considered, with different slope angles ranging from very low to very high. Steep slopes often lead to rapid runoff, which can exacerbate flood risk. ‘Average Annual Rainfall’ is another key factor, partitioned into categories from very low to very high. Moderate rainfall areas are predominant at 41.47%, making them a focal point in the assessment. Rainfall patterns have a substantial influence on flooding, with intense rainfall events increasing the risk.

‘Drainage Density’ categorizes the density of watercourses, ranging from very low to very high. ‘Moderate’ DD zones are widespread, covering 34.61% of the area. DD influences how efficiently water can flow away from an area during heavy rainfall or floods. In terms of ‘LULC’ the study area comprises various risk levels from very low to very high. ‘Cropland’ dominates at 41.85%, reflecting the substantial role of land use in flood risk assessment. The ‘Distance from Roads’ is classified from very low to very high. Significantly, ‘Very Low’ distances make up 1.93%, being the most frequent class. Conversely, river proximity is an element of importance in flood hazard study as flows and the capacity of streams for drainage influence the risk. Ultimately, approximately 2.06% of the study area corresponds to ‘Very Low’ distances. Flood vulnerability analysis takes into consideration several things, one of them being proximity to rivers that are likely to overflow. The categorized Risk Ranks and thus this way of classifying gives a good basis for knowing where to look first when assessing flood risks as well as informational support for the development and organization of disaster mitigation policies and programmes in the study area.

The study provides a comprehensive flood risk assessment using GIS and MCDA, but several limitations must be acknowledged. First, the model applies a uniform weightage to various flood risk factors across the entire country. Given India's vast and diverse landscape, this approach may not fully capture the spatiotemporal variations inherent in different regions. Factors such as topography, climate, and human development vary significantly across India, and a single model might oversimplify these complexities. Additionally, the accuracy of flood risk maps heavily depends on the quality and resolution of the data used. In some regions, data may be outdated, incomplete, or of lower resolution, potentially impacting the reliability of the model's outputs. High-resolution data specific to smaller regions are necessary for more precise assessments. The model does not fully account for temporal variations, such as seasonal changes, annual climate variability, and long-term climate change impacts. Flood risk is dynamic and can fluctuate over time, necessitating models that can integrate temporal data for more accurate predictions. Moreover, socioeconomic factors, such as infrastructure quality, population density, and preparedness levels, are complex and region-specific. The model's approach to these factors may be overly simplistic, not fully capturing the nuanced impacts of human development and socioeconomic conditions on flood risk. Validation of the model against real-time flood events and historical data is ongoing, requiring continuous calibration and refinement to ensure accuracy and reliability across diverse regions of India. The initial results need to be corroborated with more localized studies and expert feedback. Lastly, the model's scalability and adaptability to different regions within India are limited in its current form. Regional studies and sub-models tailored to specific areas are necessary to enhance their relevance and accuracy. Adapting the model to local contexts will require significant effort and collaboration with regional stakeholders and experts.

Future research directions will focus on enhancing the flood vulnerability assessment framework by integrating additional critical factors and methodologies. Factors such as soil type, deforestation rates, and infrastructure density, which significantly influence flood susceptibility, will be prioritized for inclusion. Incorporating these variables will provide a more comprehensive understanding of flood risk dynamics and improve the accuracy of our assessments. Moreover, there will be a concerted effort to analyse historical flood events to identify areas with recurrent vulnerabilities and patterns. This historical perspective will guide the refinement of our models and the development of proactive flood risk management strategies. Advanced GIS techniques and high-resolution remote sensing data will be leveraged to enhance spatial resolution and accuracy, enabling better capture of local-scale variations. Engagement with local communities and close collaboration with stakeholders, including government agencies and Non-Governmental Organizations (NGOs) will be crucial. Their insights and local knowledge will inform data collection efforts and ensure the relevance and applicability of our flood risk assessments. Furthermore, integrating climate change scenarios into our assessments will enable us to anticipate future flood risks and support adaptation strategies. Validation exercises against observed flood events and comparisons with existing flood risk assessments will be undertaken to ensure the reliability and effectiveness of our methodology. This validation process will drive ongoing refinement and improvement of our models. Ultimately, the aim is to provide decision support tools and actionable insights to policymakers and planners, facilitating informed decisions on flood mitigation, emergency response planning, and resilient infrastructure development.

Comprehensive analysis of the flood risk assessment in India has shown complex interrelationships among meteorological, geographic, and human factors contributing towards this existing problem of floods. Using GIS and MCDA, the research has been successful in offering significant knowledge on the intricate aspects of flood risk in India. Research reveals that there are several triggers of flood hazard in India and the three main ones include slope, elevation, and average yearly rainfall. Monsoons are characterized by large amounts of rainfall that come along annually in India, which makes it vulnerable to flooding. Other factors such as lower altitude and flat slopes can also increase the risk of floods to an area. Moreover, DD, LULC, and proximity to roads and rivers complicate this intricate topography of flood hazard. Each criterion was assigned a weight, according to its importance in relation to the others, thus constructing the flood risk map. The maps show different levels of flood risks that range from very low level to the highest level all over India. 3.40% of the area has been determined to be at a very high risk, while 32.65% comprises high-risk areas, 39.72% moderate risk, and 20.97% low-risk areas.

The participants have consented to the submission of the paper to the journal.

Y.P.S. conceptualized the process, and developed the methodology, rendered support in technical investigation and data collection, validated and visualized the work, wrote the original draft and reviewed the article; V.K. and D.K.T conceptualized the process, developed the methodology, visualized and supervised the study, reviewed the article; K.V.S., A.P., and D.J.M. rendered support in data collection, reviewed the article; A.P. and D.J.M. wrote the reviewed and edited the article. All authors read and approved the final version of the paper.

The authors declare that no funds, grants, or other support were received during the preparation of this paper.

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

The authors declare there is no conflict.