Non-revenue water (NRW) in Luangprabang City, Laos, has been high and fluctuating. Therefore, we aimed to analyze the factors influencing the fluctuation of NRW in two district-metered areas (DMAs). The average NRWs for 16–17 months in DMA-1 and DMA-2 were 28.92 and 43.92%, respectively, whereas the coefficients of variation of the monthly NRWs were high at 49.7 and 11.7%, respectively. Among the factors causing the fluctuation of NRW, meter inaccuracies were less than 2%, although inaccessibility to customer meters was high at 46.4 and 38.7% in DMA-1 and DMA-2, respectively. However, the meter reading intervals had little influence on billed water consumption. Using the IWA Water Balance table, the apparent loss was estimated as 2.6%, whereas the real loss (24.9%) was the main component of NRW (27.5%) in DMA-2. The monthly and 3–7-month moving averages of NRW were inversely correlated with billed water consumption, indicating that both volumetric and percentage NRWs were strongly influenced by fluctuations in billed water consumption. Network simulation verified that high inaccessibility to customer meters, particularly during the COVID-19 lockdown, caused large fluctuations in billed water consumption and NRWs. Therefore, access to customer water meters should be improved to alleviate the fluctuation of NRW.

  • High rates of meter malfunction and inaccessibility caused NRW fluctuation.

  • Apparent loss was only 9.4%, whereas real loss was 90.6% for non-revenue water.

  • Volumetric and percentage NRWs are inversely correlated with billed consumption.

  • Taking moving averages helps find relations between water consumption and NRW.

  • Meter reading errors can be estimated using DMA inflow data and EPANET simulation.

The 2030 Agenda for Sustainable Development Goal 6 calls for access to safe water and sanitation for all (Ortigara et al. 2018). Although urban water demand is expected to increase by 80% by 2050 (Flörke et al. 2018), developing new water resources remains difficult. Water loss, which is often reported as non-revenue water (NRW), is high in many cities. NRW comprises unbilled authorized consumption and water losses, which are further divided into apparent (or commercial) losses and real (or physical) losses (Lambert & Hirner 2000; Al-Omari 2013). Real water losses arise from leaks in distribution networks (Jang 2018) and represent the largest fraction of NRW (Selek et al. 2018); however, reliable data on water losses are limited in many water utilities (Liemberger & Wyatt 2019). Although high NRW rates impede sustainability, comprehensive strategies to address this issue are lacking (Farouk et al. 2023).

Kizilöz (2021) found that high pressure and pipe aging in distribution networks increase leakage in water distribution systems. Other factors, including increases and decreases in customer connections, malfunctioning and reading errors of water meters, and variations in water consumption, influence fluctuations in NRW (Güngör-Demirci et al. 2018; Ncube & Taigbenu 2019). Farley & Trow (2003) found that high consumption decreases the NRW% and vice versa, leading the American Water Works Association to discontinue support for NRW percentage indicators, as percentages are not actionable and fluctuate because of their nature as ratio indicators (AWWA 2003, 2019); instead, they promoted two new key performance indicators: the loss cost rate (value per service connection per year) and normalized water losses (water loss volume per service connection per day) (AWWA 2020). Recently, we have proved that fluctuations in billed water consumption have a considerable influence on NRW fluctuations using the data obtained for the DMAs in Colombo City, Sri Lanka, because fluctuation in water consumption influences water flow rates in the networks, causing variations of pressure in the supply networks (Pathirane et al. 2024). In theory, NRW decreases with increasing water consumption because of reductions in supply pressure, and vice versa, which was proved by the network simulation.

Understanding and evaluating the factors influencing water loss are fundamental to implement NRW reduction programs (van den Berg 2015; Jang & Choi 2017). However, in the previous studies in the literature, although each of the influencing factors on NRW fluctuations was investigated, none compared multiple factors influencing the fluctuation of NRW (Şisman & Kızılöz 2020; Chawira et al. 2022). Therefore, this study aimed to analyze and compare the multiple factors influencing the fluctuation of NRW, including meter reading inaccuracy, meter reading intervals, inaccessibility to customer meters, fluctuation of water consumption, and pressure in the supply networks.

Luangprabang City (LPBC) is the ancient capital of Laos and the provincial city of Luangprabang Province located in the central-northern part of Laos. NRW in the Luangprabang Water Supply State-owned Enterprise (LWSSE), Laos, is high and fluctuating (LWSSE 2020). However, the causes of high fluctuations in NRW remain unclear. Therefore, this study was conducted to analyze and compare the factors influencing fluctuations in NRW in the LWSSE. The data on pressure at the inlet to and water flow into the district metered areas (DMAs) of the water supply networks, monthly billed water consumption, and meter reading intervals within the DMAs were obtained from LWSSE. The accuracy of customer water meters in the DMAs was tested. The accuracy of reported billed water consumption, namely over- or under-estimation of the actual water consumption, was verified by the network simulation using the data on water supply networks.

Study area

The population of LPBC was 128,513 in 2020. The water supply coverage in LPBC was 83.89%, and the daily water supply volume was 56,800 m3/d. The LWSSE supplies piped water to the central zone, whereas two private companies, namely, Asia Co., Ltd, and DEMCO De Laos Co., Ltd, supply piped water to the northern and southern zones of LPBC since 2015. The study area is in the southern water service zone of the LPBC, where the LWSSE supplies water in collaboration with a private company, DEMCO De Laos Co., Ltd. Figure 1(a) and 1(b) shows the LWSSE supply and study areas, respectively. The two DMAs, namely DMA-1 and DMA-2, in the study area, have total distribution pipe lengths of 1,593 and 1,083 m and 195 and 77 service connections, respectively.
Figure 1

(a) Water supply area of LWSSE, and (b) DMA-1 and DMA-2.

Figure 1

(a) Water supply area of LWSSE, and (b) DMA-1 and DMA-2.

Close modal

Estimation of NRW in the DMAs

Inflow meters were installed at the inlets of the DMAs (di Nardo & di Natale 2011). NRW (m3) was calculated by subtracting billed water consumption from the DMA inflow volume (Equation (1)), and NRW (%) was calculated using 17-month data from January 2021 to May 2022 volume (Equation (2)). Inflow meter data for the DMAs were collected from official reports of the LWSSE. Billed water consumption data were obtained from 195 and 77 customers in DMA-1 and DMA-2, respectively.
formula
(1)
formula
(2)

Moving averages and statistical analysis

Moving averages of billed water consumption and NRWs were calculated for 3, 5, and 7 months; NRW was plotted against the billed water consumption for linear correlation analysis using R v. 4.3.0 (R Core 2021). The correlation between the two parameters was considered statistically significant when p < 0.05.

Customer meter testing

From August to September 2022, the accuracies of 135 and 65 customer meters among 195 and 77 meters, respectively, were tested in DMA-1 and DMA-2, respectively, using a flow rate testing instrument (TR-IV, Aichi Tokei Denki Co., Nagoya, Japan) because of limitations in access to the meters. The LWSSE sets the acceptable range of meter accuracy to ±5.0% following the standard ISO4064-1 for the accuracy of customer meters at low flow rates (International Organization for Standardization (ISO) 2017; Ferrante et al. 2022).

Effects of meter reading intervals on billed water consumption

The LWSSE designates the customer meter reading date as between the 10th and 28th of each month. Thus, variations in the meter reading intervals may have caused fluctuations in the monthly billed water consumption. Therefore, the effect of meter reading intervals on billed water consumption was evaluated using monthly data from January 2021 to May 2022 for 16 months (excluding February 2021 because of meter malfunctions) in DMA-1 and 17 months in DMA-2.

Estimation of real and apparent losses

Using the IWA Water Balance table, the main cause(s) of NRW were analyzed in DMA-2. We measured the minimum night flow, which represents real water losses (Farley et al. 2001; Liemberger & Farley 2004; Puust et al. 2010), using a portable ultrasonic flow meter (UFP-20, FUJI TECOM Co., Tokyo, Japan) in DMA-2, where water consumption at night was negligible because the area is residential.

Factors influencing NRW fluctuation evaluated using network simulation

The influence of water demand and inlet pressure variation on water losses in DMA-1 was estimated through network simulation using EPANET 2.2. The inlet pressure of DMA-1 was measured using a portable pressure recorder (FJN-501, FUJI TECOM Co.); however, the pressure was uncontrolled and unmeasured during the data acquisition period from January 2021 to May 2022. Since August 2022, the inlet pressure has been maintained at 0.335–0.355 MPa using a pressure-regulating valve.

A network simulation model for DMA-1 was constructed using the pipe network data obtained from the LWSSE (Figure 1(b)). The monthly billed water consumption data were used to calculate the 16-month average, minimum, and maximum consumption from January 2021 to May 2022 (excluding February 2021, because of the inflow meter malfunction).

Water demand at each node was calculated as the sum of the billed water consumption of the houses allocated to the nodes. We set 12 nodes in the simulation model, and the total pipe length was 1,593 m (Table A1).

The simulation was run using an average inlet pressure of 0.345 MPa and an average water demand of 6.719 m3/h. The simulation was then run under the following two conditions to evaluate the influence of water demand and inlet pressure on water loss:

  • (1)

    Fixed inlet pressure at 0.345 MPa, and varied monthly water demand from a minimum of 5.758 m3/h (February 2022) to a maximum of 8.925 m3/h (April 2021).

  • (2)

    Fixed water demand at 6.719 m3/h, and varied inlet pressure at five levels: 0.145, 0.245, 0.345, 0.445, and 0.545 MPa.

NRWs in the DMAs

The average NRWs for 16 and 17 months in DMA-1 and DMA-2 were 28.92 and 43.92%, respectively, higher than the previously reported NRW of 28% for the entire LWSSE service area.

Figure 2 shows the fluctuations in the monthly inflow, billed water consumption, and NRW in DMA-1. Data from February 2021 were excluded because of an inflow meter malfunction. Among these three parameters, NRW showed the most significant fluctuations of between 1.14% in April and 52.65% in November 2021, with a coefficient of variation (CV) of 49.7%. The CVs for billed water consumption and inflow were 12.8 and 12.6%, respectively. Similar results were obtained for DMA-2, as shown in Figure A1.
Figure 2

Monthly variation of inflow, billed water consumption, and NRW in DMA-1 from January 2021 to May 2022 (excluding February 2021).

Figure 2

Monthly variation of inflow, billed water consumption, and NRW in DMA-1 from January 2021 to May 2022 (excluding February 2021).

Close modal
Figure 3 shows the correlation between billed water consumption and NRW using monthly and 3-, 5-, and 7-month moving average data. As evidenced by the negative values of correlation coefficients (r), NRW% and NRW-m3 were inversely correlated with billed water consumption in all of the four figures (Figure 3(a)–3(d)). Furthermore, the monthly NRW varied considerably with the correlation coefficients at −0.840 and −0.735 for NRW% and NRW-m3, respectively (Figure 3(a)). The use of moving averages reduced NRW variations in the monthly data, as shown by the increasing absolute values of the correlation coefficients (r) to −0.992 and −0.977 for NRW% and NRW-m3, respectively, with the 7-month moving average (Figure 3(d)). These results indicate that taking 3–7-month moving averages mitigates fluctuations of monthly data and helps identify the true relationship between billed water consumption and NRW.
Figure 3

Correlation between billed water consumption and NRW-m3 or NRW% in DMA-1. (a) Monthly; (b) 3-month moving averages (MA); (c) 5-month MA; and (d) 7-month MA. Data period: 16 months from January 2021 to May 2022 (excluding February 2021).

Figure 3

Correlation between billed water consumption and NRW-m3 or NRW% in DMA-1. (a) Monthly; (b) 3-month moving averages (MA); (c) 5-month MA; and (d) 7-month MA. Data period: 16 months from January 2021 to May 2022 (excluding February 2021).

Close modal

Customer meter testing

In DMA-1, metering inaccuracies ranged from −72.92 to +25.20%, with an average of −1.66% (Figure A2a). Of the 135 tested meters, 7 meters had inaccuracies exceeding 5%, 14 meters fell below −5%, and the remaining 114 meters were within 5%. Similarly, in DMA-2 (Figure A2b), the metering inaccuracies ranged from −37.41 to +40.35%, with an average of −1.97%. Of the 65 tested meters, 4 meters had inaccuracies that exceeded 5%, 11 meters fell below −5%, and the remaining 50 meters were within 5%. The accuracy of mechanical water meters is reported to be in the range of 1.0–2.0% (Farley et al. 2008). Although Infantri Yekti et al. (2019) measured the accuracy of 23 customer meters and found that the average inaccuracy was 0.214%, Chen et al. (2023) estimated the error to be 3.7%. While the average inaccuracies of the two DMAs were within 2.0% in this study, some water meters were registering high inaccuracies. Thus, the regular testing of customer meters and the replacement of meters registering high inaccuracies are strongly recommended.

Customer metering inaccuracies

Although the average meter inaccuracies were within 2%, as mentioned above, meter reading inaccuracies arose from factors such as inaccessibility to water meters, errors in meter reading and data entry, meter malfunctioning, and customer tampering with meters.

In DMA-1, of 195 customer meters, 60 meters were inaccessible (30.8%); of the 135 meters tested, 21 (15.6%) were malfunctioning (Figure A3a), totaling 46.4% inaccessible or malfunctioning meters. In DMA-2, of the 77 meters, 12 were inaccessible (15.6%), and of the 65 meters tested, 15 meters (23.1%) were malfunctioning (Figure A3b), totaling 38.7% of meters. This high rate of inaccessible or malfunctioning customer meters is among the major causes of inaccuracies and possible fluctuations in billed water consumption and NRW.

Such high ratios of meter malfunctioning arise from customers frequently opening and closing taps to store water in tanks (Figure A4a) and sediment deposition inside meters because of poor water quality and infiltration of dirt during pipe repairs (Figure A4b). Meter tampering or small pieces of objects inside the meters can disrupt moving parts and lead to inaccuracies in meter readings (Figure A4c).

Effects of meter reading intervals on billed water consumption

Figure 4(a) and 4(b) shows that we found no significant correlation between the meter reading intervals and billed water consumption in DMA-1 (r = 0.051, p> 0.05) and DMA-2 (r = −0.180, p> 0.05). Instead, billed water consumption increased during festival months: April (Lao New Year), August (Boat Racing), and December (National Day − Year End) in 2021–2022, indicating that people consume more water during festival periods (Figure A5).
Figure 4

Effects of meter reading intervals on billed water consumption: (a) DMA-1; (b) DMA-2. Data period: DMA-1, 16 months from January 2021 to May 2022 (excluding February 2021); DMA-2, 17 months from January 2021 to May 2022.

Figure 4

Effects of meter reading intervals on billed water consumption: (a) DMA-1; (b) DMA-2. Data period: DMA-1, 16 months from January 2021 to May 2022 (excluding February 2021); DMA-2, 17 months from January 2021 to May 2022.

Close modal

Estimation of apparent and real losses

In DMA-2, the inlet pressure decreased during periods of high flow rates and vice versa (Figure A6). The pressure was 0.34–0.37 MPa, and the minimum night flow was 0.60 m3/h at 1:14 AM on September 20, 2022, which was considered a real loss.

Figure A7 shows the correlation between the water pressure and inflow at the DMA-2 inlet. The flow rates varied extensively at low pressure (0.34 MPa during the day), whereas these values were low and did not vary at high pressure (0.37 MPa during the night).

In DMA-2, the apparent (commercial) loss was 2.6% (Table A2), which is slightly higher than the average metering inaccuracy of 1.97% (Figure A2b). The real (physical) loss was 24.9% of the overall 27.5% NRW. Thus, the apparent loss was only 9.4% of the overall NRW, indicating that decreasing real loss is important for reducing NRW. The average apparent loss in Asian countries was estimated to be 25.4% of the total NRW; thus, the ratio of apparent loss in our study was less than the average (Frauendorfer & Liemberger 2010). Al-Washali et al. (2020) compared various methods of estimating apparent and real water losses, and found that different methods estimate the water loss components differently. Thus, they suggested using at least two different methods to estimate water loss components. Therefore, although we used the minimum night flow method in this study to estimate the real loss, we should note that the estimated amounts of real loss are dependent on the method we employed.

Evaluating factors influencing NRW fluctuation by network simulation

Table 1 shows the results of network simulation in DMA-1. First, the inlet pressure and water demand were set to 16-month averages of 0.345 and 6.719 m3/h, respectively. Simulated inflow and water loss were 9.450 m3/h and 28.90%, respectively, which are close to the actual values of 9.453 m3/h and 28.92%, respectively.

Table 1

Influence of inlet pressure on inflow and water loss by network simulation (DMA-1)

Inlet pressure (MPa)  Simulated pressure (MPa)
Water demand (m3/h)Inflow (m3/h)
Water loss (m3/h)
Water loss (%)
MinimumMaximumActualSimulatedActualSimulatedActualSimulated
0.145 0.136 0.198 6.719  8.530  1.811  21.23 
0.245 0.234 0.297 6.719  9.020  2.301  25.51 
0.345a 0.334 0.396 6.719b 9.453b 9.450 2.734b 2.731 28.92b 28.90 
0.445 0.433 0.496 6.719  9.820  3.101  31.58 
0.545 0.532 0.595 6.719  10.120  3.401  33.61 
Inlet pressure (MPa)  Simulated pressure (MPa)
Water demand (m3/h)Inflow (m3/h)
Water loss (m3/h)
Water loss (%)
MinimumMaximumActualSimulatedActualSimulatedActualSimulated
0.145 0.136 0.198 6.719  8.530  1.811  21.23 
0.245 0.234 0.297 6.719  9.020  2.301  25.51 
0.345a 0.334 0.396 6.719b 9.453b 9.450 2.734b 2.731 28.92b 28.90 
0.445 0.433 0.496 6.719  9.820  3.101  31.58 
0.545 0.532 0.595 6.719  10.120  3.401  33.61 

aAverage inlet pressure between 0.335 and 0.355 MPa.

bAverage data for 16 months from January 2021 to May 2022 (excluding February 2021) in DMA-1.

To analyze the influence of inlet pressure variation on water loss, the inlet pressure was varied from 0.145 to 0.545 MPa, while the average water demand was kept fixed. Because the inlet pressure influenced the pressure at all nodes, DMA inflow (8.530–10.120 m3/h) and water loss (1.811–3.401 m3/h, 21.23–33.61%) were also influenced by the inlet pressure. Thus, controlling the inlet pressure is very important for reducing NRW because water loss and consumption can be reduced by controlling the inlet pressure (Vicente et al. 2016; Karakatsanis & Theodossiou 2022). Flow control using a pressure-reducing valve and active pressure control can reduce leakage in water distribution systems (Kowalski & Suchorab 2023).

Then, to analyze the influence of water consumption fluctuations on water loss, the monthly water demand was varied from the minimum (5.758 m3/h) to maximum (8.925 m3/h) values of meter reading during the study period, while the inlet pressure was fixed at an average value of 0.345 MPa (Table 2). The results show that the simulated inflow (8.490 and 11.650 m3/h) and water loss (2.732 and 2.725 m3/h, 32.18 and 23.39%), which are underlined in Table 2, were greater than the reported values based on meter reading, indicating that the water demand was overestimated by the meter readers because of inaccessibility to water meters during the COVID-19 lockdown periods. Thus, assuming that the DMA inflow measured by the water flow meter was correct, the actual water demand was estimated using the simulation (bold figures in Table 2). In the minimum and maximum water demand cases, water demand was overestimated by 13.7 and 41.8%, respectively, and water losses (%) were underestimated by 8.87 and 29.08% by the meter readers. Thus, meter reading errors because of meter malfunctioning and inaccessibility to customer meters, particularly during the COVID-19 lockdown period, were the main causes of erroneous estimations and fluctuations in billed water consumption and water losses.

Table 2

Effect of water demand on inflow and water loss by network simulation (DMA-1)

Inlet pressure (MPa)Simulated pressure (MPa)
Water demandInflow (m3/h)
Water loss (m3/h)
Water loss (%)
MinimumMaximum(m3/h)ActualSimulatedActualSimulatedActualSimulated
0.345 0.336 0.398 5.758a 7.800 8.490 2.042 2.732 26.18 32.18 
 0.337 0.399 5.066b  7.800  2.734  35.05 
0.345 0.334 0.396 6.719c 9.453 9.450 2.734 2.731 28.92 28.90 
0.345 0.328 0.393 8.925d 9.028 11.650 0.103 2.725 1.14 23.39 
 0.335 0.397 6.294e  9.020  2.726  30.22 
Inlet pressure (MPa)Simulated pressure (MPa)
Water demandInflow (m3/h)
Water loss (m3/h)
Water loss (%)
MinimumMaximum(m3/h)ActualSimulatedActualSimulatedActualSimulated
0.345 0.336 0.398 5.758a 7.800 8.490 2.042 2.732 26.18 32.18 
 0.337 0.399 5.066b  7.800  2.734  35.05 
0.345 0.334 0.396 6.719c 9.453 9.450 2.734 2.731 28.92 28.90 
0.345 0.328 0.393 8.925d 9.028 11.650 0.103 2.725 1.14 23.39 
 0.335 0.397 6.294e  9.020  2.726  30.22 

Figures are described in the text and are both bold and underlined.

aMinimum water demand in February 2022.

bSimulated water demand.

cAverage water demand in 16 months from January 2021 to May 2022 (excluding February 2021).

dMaximum water demand in April 2021.

eSimulated water demand.

Although previous studies evaluated one of the factors influencing NRW, this study contributed to the literature by presenting the methods to evaluate and compare multiple factors influencing the fluctuation of NRW. Variations in meter reading intervals did not influence billed water consumption. Rather, major events for citizens influenced water consumption. Meter testing revealed that the average metering inaccuracies were within 2%. However, the ratios of meter malfunction and inaccessibility were high at 46.4% and 38.8 in DMA-1 and DMA-2, respectively. Meter malfunctioning is caused by customers tampering with meters, accumulation of sediment inside meters, and clogging of small objects inside meters. The analysis of NRW using the IWA Water Balance table revealed that the apparent (commercial) loss was only 9.4% of the total NRW, whereas the real (physical) loss was 90.6% of the total NRW, demonstrating that real (physical) loss is the main cause of the high NRW.

Monthly NRW was inversely correlated with monthly billed water consumption, as shown by plotting the correlation between billed water consumption and NRW using 3–7-month moving averages. The results of the network simulation verified that fluctuations in the billed water consumption and inlet pressure strongly influenced fluctuations in the NRW. Controlling the DMA inlet pressure to counter the fluctuation in water consumption can stabilize the network pressure and thereby reduce NRW. In addition, this study presented the network simulation method to estimate the errors in reporting billed water consumption, which was found to be the main cause of inaccuracies and fluctuations in billed water consumption and NRW. Therefore, it is important to improve meter reading accuracy by replacing malfunctioning water meters and providing easy access to customer meters. The limitation of this study was that the data period was rather short: thus, it is recommended to take data for longer periods. The method to estimate inaccuracies of billed water consumption presented in this paper can be applied to DMAs of any other cities with fluctuating NRWs.

The authors thank the Luangprabang Water Supply State-Owned Enterprise for their support in conducting the meter testing survey and data collection.

This research was funded by the Japan International Cooperation Agency (JICA), which also provided a scholarship to Sunti Chandaeng for his graduate studies. This study was also funded by a grant-in-aid for scientific research (No. 22H01621) provided by the Japan Society for the Promotion of Sciences (JSPS).

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

The authors declare there is no conflict.

Al-Omari
A.
2013
A methodology for the breakdown of NRW into real and administrative losses
.
Water Resources Management
27
,
1913
1930
.
https://doi.org/10.1007/s11269-013-0262-y
.
Al-Washali
T.
,
Sharma
S.
,
Lupoja
R.
,
Al-Nozaily
F.
,
Haidera
M.
&
Kennedy
M.
2020
Assessment of water losses in distribution networks: Methods, applications, uncertainties, and implications in intermittent supply
.
Resources, Conservation and Recycling
152
,
104515
.
https://doi.org/10.1016/j.resconrec.2019.104515
.
AWWA
2003
Best Practice in Water Loss Control: Improved Concepts for 21st Century Water Management
. .
AWWA (American Water Works Association)
2019
Key Performance Indicators for Non-Revenue Water
.
AWWA Technical and Education Council's Water Loss Control Committee
. .
AWWA Water Loss Control Committee
2020
Committee report: key performance indicators for nonrevenue water – AWWA's 2020 position
.
Journal-American Water Works Association
112
(
1
),
20
30
.
https://doi.org/10.1002/awwa.1428
.
Chawira
M.
,
Hoko
Z.
&
Mhizha
A.
2022
Partitioning non-revenue water for Juru Rural Service Centre, Goromonzi District, Zimbabwe
.
Physics and Chemistry of the Earth, Parts A/B/C
126
,
103113
.
https://doi.org/10.1016/j.pce.2022.103113
.
Chen
H.-L.
,
Lo
S.-L.
,
Kuo
J.
&
Huang
C.-L.
2023
Estimate measurement errors of household water meters using a large amount of on-site data feedback
.
Sustainable Environment Research
33
,
19
.
https://doi.org/10.1186/s42834-023-00180-z
.
di Nardo
A.
&
di Natale
M.
2011
A heuristic design support methodology based on graph theory for district metering of water supply networks
.
Engineering Optimization
43
(
2
),
193
211
.
https://doi.org/10.1080/03052151003789858
.
Farley
M.
&
Trow
S.
2003
Losses in Water Distribution Networks
.
IWA publishing
. .
Farley
M.
,
Water
S.
,
Supply
W.
&
Council
S. C.
&
World Health Organization
2001
Leakage Management and Control: A Best Practice Training Manual (No. WHO/SDE/WSH/01.1)
. .
Farley
M.
,
Wyeth
G.
,
Ghazali
Z.-b.
,
Istandar
A.
&
Singh
S.
2008
The Manager's Non-Revenue Water Handbook – A Guide to Understanding Water Losses, United States Agency for International Development (USAID)
.
Farouk
A. M.
,
Rahman
R. A.
&
Romali
N. S.
2023
Non-revenue water reduction strategies: A systematic review
.
Smart and Sustainable Built Environment
12
(
1
),
181
199
.
https://doi.org/10.1108/SASBE-04-2021-0071
.
Ferrante
M.
,
Rogers
D.
,
Mugabi
J.
&
Casinni
F.
2022
Impact of intermittent water supply on water meter accuracy
.
AQUA – Water Infrastructure, Ecosystem and Society
71
(
11
),
124
.
https://doi.org/10.2166/aqua.2022.091
.
Flörke
M.
,
Schneider
C.
&
McDonald
R. I.
2018
Water competition between cities and agriculture driven by climate change and urban growth
.
Nature Sustainability
1
(
1
),
51
58
.
https://doi.org/10.1038/s41893-017-0006-8
.
Frauendorfer
R.
&
Liemberger
R.
2010
The Issues and Challenges of Reducing Non-Revenue Water
.
Asian Development Bank
.
Güngör-Demirci
G.
,
Lee
J.
,
Keck
J.
,
Guzzetta
R.
&
Yang
P.
2018
Determinants of non-revenue water for a water utility in California
.
Journal of Water Supply: Research and Technology – AQUA
67
(
3
),
270
278
.
https://doi.org/10.2166/aqua.2018.152
.
Infantri Yekti
M.
,
Norken
I.-N.
&
Wentiari
N.-P.-R.
2019
Non-revenue water (NRW) and its handline for a drinking water supply system in Kedewatan zone Gianyar Bali
.
MATEX Web of Conferences
276
,
04004
.
https://doi.org/10.1051/matecconf /201927604004
.
ISO4064-1
.
2017
Water Meters for Cold Potable Water and Hot Water – Part 1: Metrological and Technical Requirements
.
International Organization for Standardization
,
Geneva
.
Jang
D.
2018
A parameter classification system for nonrevenue water management in water distribution networks
.
Advances in Civil Engineering
2018
.
https://doi.org/10.1155/2018/3841979
.
Jang
D.
&
Choi
G.
2017
Estimation of non-revenue water ratio using MRA and ANN in water distribution networks
.
Water
10
(
1
),
2
.
https://doi.org/10.3390/w10010002
.
Kizilöz
B.
2021
Prediction model for the leakage rate in a water distribution system
.
Water Supply
21
(
8
),
4481
4492
.
https://doi.org/10.2166/ws.2021.194
.
Kowalski
D.
&
Suchorab
P.
2023
The impact assessment of water supply DMA formation on the monitoring system sensitivity
.
Applied Sciences
13
(
3
),
1554
.
https://doi.org/10.3390/app13031554
.
Lambert
A.
&
Hirner
W.
2000
Losses From Water Supply Systems: A Standard Terminology and Recommended Performance Measures
.
IWA
. .
Liemberger
R.
&
Farley
M.
2004
Developing a nonrevenue water reduction strategy. Part 1: Investigating and assessing water losses
. In
Paper to IWA Congress
. .
Liemberger
R.
&
Wyatt
A.
2019
Quantifying the global non-revenue water problem
.
Water Supply
19
(
3
),
831
837
.
https://doi.org/10.2166/ws.2018.129
.
Luangprabang Water Supply State-Owned Enterprise
2020
The Official Annual Report (in Lao)
.
Ncube
M.
&
Taigbenu
A.
2019
Assessment of apparent losses due to meter inaccuracy – A comparative approach
.
Water SA
45
(
2
),
174
182
.
https://doi.org/10.2166/ws.2018.178
.
Ortigara
A. R. C.
,
Kay
M.
&
Uhlenbrook
S.
2018
A review of the SDG 6 synthesis report 2018 from an education, training, and research perspective
.
Water
10
(
10
),
1353
.
https://doi.org/10.3390/w10101353
.
Pathirane
A.
,
Kazama
S.
&
Takizawa
S.
2024
Dynamic analysis of non-revenue water in district metered areas under varying water consumption conditions owing to COVID-10
.
Heliyon
20
,
e23516
.
https://doi.org/10.1016/j.heliyon.2023.e23516
.
Puust
R.
,
Kapelan
Z.
,
Savic
D. A.
&
Koppel
T.
2010
A review of methods for leakage management in pipe networks
.
Urban Water Journal
7
(
1
),
25
45
.
https://doi.org/10.1080/15730621003610878
.
R Core Team
2021
A Language and Environment for Statistical Computing
.
R Foundation for Statistical Computing
,
Vienna, Austria
.
Available from: https://www.R-project.org/ (accessed 11 May 2023)
.
Selek
B.
,
Adigüzel
A.
,
İritaş
Ö.
,
Karaaslan
Y.
,
Kinaci
C.
,
Muhammetoğlu
A.
&
Muhammetoğlu
H.
2018
Management of water losses in water supply and distribution networks in Turkey
.
Turkish Journal of Water Science and Management
2
(
1
),
58
75
.
https://doi.org/10.31807/tjwsm.354298
.
Şisman
E.
&
Kızılöz
B.
2020
Trend-risk model for predicting non-revenue water: An application in Turkey
.
Utility Policy
67
,
101137
.
https://doi.org/10.1016/j.jup.2020.101137
.
van den Berg
C.
2015
Drivers of non-revenue water: A cross-national analysis
.
Utilities Policy
36
,
71
78
.
https://doi.org/10.1016/j.jup.2015.07.005
.
Vicente
D. J.
,
Garrote
L.
,
Sánchez
R.
&
Santillán
D.
2016
Pressure management in water distribution systems: Current status, proposals, and future trends
.
Journal of Water Resources Planning and Management
142
(
2
),
04015061
.
https://doi.org/10.1061/(ASCE)WR.1943-5452.0000589
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Supplementary data