Urban Water Resources Research Articles

Assessment of water conservation and water intensification level based on AHP-fuzzy comprehensive evaluation model

Scientific evaluation of water conservation and water intensification level is an important prerequisite for addressing water resource shortage. In the current research, there are problems of confusing evaluation indicator systems and unscientific evaluation results. The paper first constructed the evaluation indicator systems of water conservation and water intensification based on the different connotations of the two, and then evaluated the utilization level of the two respectively by using AHP-fuzzy comprehensive evaluation model. The results indicated that from 2010 to 2022, the level of water conservation and water intensification in Zhengzhou both showed an upward trend, but the conservation level was higher than that of intensification. Therefore, promoting water intensification is the key to realize scientific water use in Zhengzhou in the future. Improving conservation level should prioritize the improvement of agricultural and domestic water use, while enhancing intensification level should prioritize the improvement of comprehensive and ecological water use. Separate evaluation of the two is more conducive to identifying weaknesses in their respective processes. Thus, more targeted and scientific improvement measures can be taken to enhance the level of urban water resource development and utilization.

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

A novel approach for quantifying the influence intensity of urban water and greenery resources on microclimate for efficient utilization

Climate changes have led to increasing global energy consumption, detrimental to the sustainable development of society. Urban blue-green infrastructure (UBGI) can improve urban microclimate. However, influence intensity of UBGI on microclimate has not been quantified deeply to improve the use efficiency of water and greenery resources. To solve research deficiencies, this study numerically simulated the UBGI for 44 scenarios with different resource configurations (various water body areas and greenery coverages) in summer. Based on simulations, this study developed novel mathematical models of UBGI thermo-environment (BGTE) to quantify influence intensity of UBGI. The results indicated that daytime synergies of UBGI first increased and then decreased with time. The significance intensity of the influence of time (t), water body area (Sw), tree coverage rate (TCR), shrub coverage rate (SCR), and grassland coverage rate (GLCR) on the synergy of water and greenery was quantified by artificial neural network: t (39.4 %), Sw (22.6 %), TCR (22.0 %), SCR (13.2 %), and GLCR (2.8 %). To make the overall effect of UBGI on microclimate relatively efficient, Sw should be less than 10,000 m2, SCR should be greater than 65 %, and TCR should be close to 15 %. This research provides practical ideas for efficient use of water and greenery resources.

Rainwater runoff regulation effect in sponge reconstruction area based on rainstorm flood management model

The study adopts the rainwater regulation strategy based on the rainstorm water management model, and designs a set of rainwater runoff regulation system by monitoring the amount of water, collecting rainfall data, and evaluating the quality of rainwater and natural rainwater, aiming at conserving groundwater, restoring hydrological cycle, reducing urban waterlogging, and optimizing rainwater resources. In the implementation method, the effectiveness of this strategy is evaluated by simulating various scenarios such as without LID facilities, with LID facilities, and secondary rainwater pipe network regulation. The results showed that the Nash efficiency coefficient of the overall model was 0.89, and the changes in chemical oxygen demand, total nitrogen, and total phosphorus water quality indicators were very close to the actual monitoring results. The comparative data before and after the renovation showed that the three water quality indicators were much higher before the renovation. The peak flow rate and outflow time significantly decreased, for example, the peak flow rate decreased from 0.41 m³ /s to 0.39 m³ /s. The outflow time was reduced from 9 min to 6 min. It can be seen that the strategies proposed in the study have effectively improved the quality of urban environment and residents' lives, providing important reference basis for urban rainwater management and water resource protection.

Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress

We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.

Water function zone: A method to improve the accuracy of remote sensing retrieval of water bodies

In water monitoring, it is always a hot issue to exploring a method to improve the accuracy of remote sensing inversion. To this end, based on the actual investigation of the surrounding environment and water quality characteristics of the Fuyang river, this study has established five distinct water functional zones: reservoir (RS), village river (VR), industrial river (IR), artificial lake (AL), and urban river (UR), which are considered as subdivided datasets. Meanwhile, considering that lakes and reservoirs typically contain relatively stationary or slow-moving water bodies, while river channels are characterized by moving water bodies, three multi-type datasets have been constructed for comparison. These are based on the morphological and flow characteristics of different regions within the Fuyang river and include river channel (R), lake (L), and whole basin (W). Based on multi-spectral images of unmanned aerial vehicle (UAV) and measured water quality data, ten types of inversion models were to invert six kinds of water quality parameters, and compare and analyze the performance of the optimal inversion model of the corresponding data sets. The results show that the multivariate regression model is superior to the univariate regression model, and the mean coefficient of determination (R2) of the subdivided data set is 0.936, and the root mean square error (RMSE) and mean absolute error (MAE) appear decrease in different degrees compared with the W with 3.573 and 2.662, respectively. The mean ratio performance to interquartile (RPIQ) of the corresponding model in UR and VR are 3.963 and 2.748, respectively. Extremely Randomized Trees (ERT) model is more suitable for the inversion of multi-type data sets lacking obvious water quality characteristics and with complex components, while Categorical Boosting (CatBoost) model is more suitable for the inversion of subdivided data sets with obvious water quality characteristics. The current method has practical guiding significance for improving the application level of water monitoring technology in ecological environment protection and urban water resources protection.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Comparative analysis of greywater recycling and rainwater harvesting as supplementary water sources for conventional urban and tourist resort water supplies

Freshwater scarcity combined with high water demand from rapid urbanization, population growth, and changing consumption has resulted in increasing stress on urban water supply systems. Seasonal fluctuations in the population of tourist destinations are especially evident in tourist resorts. Greywater recycling and rainwater harvesting have been proposed as the most widely used valuable strategies to address water scarcity. By taking the conventional water supply system and the water supply of tourist resorts as examples, this study systematically compares and analyzes the advantages and limitations of greywater recycling and rainwater harvesting by enumerating the stress of water supply in different systems, discussing the benefits of both strategies and comparing the differences when using them for tourist destinations. The results showed that pumping systems or elevated tanks used to meet the water supply needs of high-rise buildings pose energy challenges. The water use characteristics of tourist resorts cause it to be closely related to the seasonal influx of tourists. Greywater recycling is more effective than rainwater harvesting in mitigating tourist resorts' water shortage problem. Suggestions for ways to implement these strategies for different regions are also given to make informed decisions about water distribution efforts. This study provides a reference for sustainable urban water resource management.

Construction and application of urban water resources spatial equilibrium model: A case study of Ulanqab City

ABSTRACT Evaluating the water resources spatial equilibrium (WRSE) is the foundation for scientifically planning the construction of an optimized water resource allocation system and achieving the high-standard development of the China Water Network Project. To elucidate the characteristics of WRSE variation in Ulanqab City, this study explored a novel WRSE assessment model based on connection number and the subtraction set pair potential method. The results indicate that: (1) there exists a mismatch between the distribution of water resources in Ulanqab City and its socio-economic development. In 2020, the Gini coefficients of water resources total quantity concerning resident population, gross domestic product (GDP), industrial output value, and irrigated land area were 0.48, 0.48, 0.51, and 0.28, respectively. (2) From 2011 to 2020, the overall WRSE level in Ulanqab City showed an increasing trend year by year. Among them, the WRSE levels in six counties improved, while Jining District consistently remained at a low equilibrium state due to the prominent contradiction between water supply and demand. (3) In the future, Ulanqab City can further enhance the WRSE level by promoting the reuse of recycled water, intensifying groundwater pollution control, and implementing a quota management system for agricultural water use.

An integrated urban water resources management approach for infrastructure and urban planning

Transportation projects are crucial for the overall success of major urban, metropolitan, regional, and national development according to their capacity by bringing significant changes in socio-economic and territorial aspects. In this context, sustaining and development of economic and social activities depend on having sufficient Water Resources Management. This research helps to manage transport project planning and construction phases to analyze the surface water flow, high-level streams, wetland sites for development of transportation infrastructure planning, implementing, maintenance, monitoring and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. A case study was carried out using the Arc Hydro extension within ArcGIS for processing and presenting spatially-referenced Stream Model. Geographical information systems have the potential to improve water resources planning and management. The study framework would be useful for water resources problem solving by enabling decision makers to collect qualitative data more effectively and gather them into the water management process by a systematic framework.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

Exploring potential trade-offs in outdoor water use reductions and urban tree ecosystem services during an extreme drought in Southern California

In Southern California cities, urban trees play a vital role in alleviating heat waves through shade provision and evaporative cooling. Trees in arid to semi-arid regions may rely on irrigation, which is often the first municipal water use to be restricted during drought, causing further drought stress. Finding a balance between efficient water use and maintaining tree health will be crucial for long-term urban forestry and water resources management, as climate change will increase drought and extreme heat events. This study aimed to quantify how urban tree water and carbon fluxes are affected by irrigation reductions, and how that relationship changes with tree species and temperature. We used an ecohydrologic model that mechanistically simulates water, carbon, and energy cycling, parameterized for 5 common tree species in a semi-arid urban area. We simulated a range of irrigation reductions based on average outdoor water use data from the city for a recent extreme drought as well as with warmer temperatures. We then analyzed the response of model outcomes of plant carbon fluxes, leaf area index (LAI), and water use. Results show that reducing irrigation up to 25%, a comparable amount as the California state mandate in 2014, has minimal effects on tree primary productivity and water use efficiency. We found that transpiration was linearly related to irrigation input, which could lead to a short-term loss of evaporative cooling with irrigation reductions during drought. However, primary productivity and LAI had a nonlinear response to irrigation, indicating shade provision could be maintained throughout drought with partial irrigation reductions. Results varied across tree species, with some species showing greater sensitivity of productivity to both irrigation reductions and potentially warmer droughts. These results have implications for water resources management before and during drought, and for urban tree climate adaptation to future drought.

POLITICAL AND ECONOMIC ASPECTS OF INTEGRATED URBAN WATER RESOURCES MANAGEMENT (IUWRM) IN SEMARANG CITY INDONESIA

Purpose: This study aims to find out the aspects of politics and economics of integrated urban water resources management in Semarang City and their impact on the practices of qualities of water resources management. Methods: This research used the qualitative method and field research approach. The focus data are the regulation and the implementation of water resources management in Semarang City Indonesia. The data was drawn from documents and interview with some resource persons. Results and Conclusion: The study found that the city government, which should carry out the function of conserving water resources, actually polluted the Kaligarang River through one of its institutions: Local Water Drinking Company, “Tirta Moedal”. This company disposes of waste sludge containing aluminum metal directly into the river without any treatment processes that impacted in pollution in the river and causing siltation of the river downstream. Research implications: the enactment of the comprehensive and integrated water resources management will has a positive impact on the practices of water resources management in Semarang specifically and Indonesia and even world countries generally. The implementation of comprehensive and integrated water resources management will also support the achievement of the sustainable development Goal. Originality/value: Semarang City government must be supported to arrange a comprehensive and integrated water resources management (WRM) policy. There must be a clear legal law for the water resources management.

Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network-Long Short-Term Memory

Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction methods mainly focus on the time dimension characteristics of the variables, while ignoring the potential influence of spatial characteristics on the temporal characteristics of the variables. This leads to low prediction accuracy. To address this problem, a short-term urban water demand prediction model which integrates both spatial and temporal characteristics is proposed in this paper. Firstly, anomaly detection and correction are conducted using the Prophet model. Secondly, the maximum information coefficient (MIC) is used to construct an adjacency matrix among variables, which is combined with a graph convolutional neural network (GCN) to extract spatial characteristics among variables, while a multi-head attention mechanism is applied to enhance key features related to water use data, reducing the influence of unnecessary factors. Finally, the prediction of short-term urban water demand is made through a three-layer long short-term memory (LSTM) network. Compared with existing prediction models, the hybrid model proposed in this study reduces the average absolute percentage error by 1.868–2.718%, showing better prediction accuracy and prediction effectiveness. This study can assist cities in rationally allocating water resources and lay a foundation for future research.

Chasing Comfort in the Chill: Simulating the Impact of Opened Streamlines on Microclimatic Conditions, Thermal Comfort and Building Facades in Cold Climate Cities

This project aims to evaluate the impact of water surfaces on urban microclimate change and the presence of water in dense urban areas on the heating and cooling loads of buildings by proposing the rehabilitation of urban water resources. It mainly questions whether favorable outcomes for urban microclimate may be expected at the local level with the rehabilitation of water bodies and the contribution of repaired water lines to energy savings in nearby buildings. The analyses are made for Erzurum, the coldest climate city in Türkiye, by using the ENVI-met program, a dynamic simulation tool widely used for microclimate analysis. The analyses use the meteorological data recorded on the site during the same years’ summer and winter periods. The microclimate change caused by the green areas, which involve the opening of covered streamlines, was estimated, and the change in the energy load of buildings was quantitatively analyzed. The findings show that rehabilitation of water lines in urban areas improved microclimate conditions and provided energy-saving opportunities for buildings. Thus, policy-makers should produce more green spaces, including water surfaces, in urban areas to improve the adaptation capacity of cities and mitigate the negative effects of climate change.

Assessing the water quality in urban river considering the influence of rainstorm flood: A case study of Handan city, China

The water quality of urban rivers is subject to fluctuation caused by rainstorm flood. The uncertainty of flooding in a dynamic environment brings about changes in river quality, presenting a significant challenge. Water samples were collected at 7 sampling sites in Handan City of China from January 2020 to August 2023, and 9 water quality parameters (WT, pH, Cond, Do, COD, BOD, NH3-N, TP, and TP) were analyzed. Specifically, the spatial and temporal variation of water quality in the Qingzhang River was analyzed. In terms of season, the concentration of water quality parameters in spring and winter was found to be significantly higher than that in summer and autumn. Spatially, the concentration of water quality parameters was lower in the upstream compared to the downstream. Furthermore, it was discovered that the reservoir had a purifying effect on the water quality of the river. Additionally, a comparison of water quality during flood and non-flood periods revealed that the concentration of water quality parameters in the upstream, midstream, and downstream of the Qingzhang River were higher during non-flood periods. These findings indicated that, in comparison to the urbanization factor, the hydrological factor, which results in the runoff carrying nutrients into the Qingzhang River, plays a crucial role in the change of its water quality. Moreover, the autoregressive integrated moving average (ARIMA) method was employed to create a water pollution emergency prediction model for the different sections of the Qingzhang River, and an adaptive urban river water purification strategy was formulated based on its variation patterns. The research results contribute to the theoretical basis for the management of urban rivers and water resources.

Change in water quality in an Amazonian microbasin: ecological and human health implications.

The decline in the quality of water resources in the Amazon is very rapid in cities suffering from unplanned urban growth. The region has two defined seasons, winter (wet) and summer (dry), which directly affect the behavior of contaminants in aquatic ecosystems. The aim of this study was to assess the ecological and human health risks associated with the use of the watershed. In addition, an ecological index was proposed: the Quality Index for Aquatic Life, for the risk of contaminants to aquatic life. Sampling was carried out at six points in the Juá watershed. Physicochemical parameters, major anions, metals and total phosphorus were analyzed at both stations between 2020 and 2021. The highest concentrations of contaminants were found in the rainy season, due to the washing away of the banks. In this sense, Cl presented a concentration more than 307 times higher than that permitted by Brazilian legislation (wet). The ecological index showed that the watershed has a high risk of metals such as Cr III and Cr VI for the biota. The human health risk analysis showed a low risk; however, the lack of basic sanitation in the city indicates that monitoring of urban water resources is necessary.

A system dynamics simulation-based strategic analysis of integrated water resources utilization and management in Shenzhen city.

As one of the most rapidly developing cities in China, Shenzhen grapples with an increasing challenge in managing water resources due to escalating conflicts with its soaring water demand. This study established a system dynamics (SD) model based on a causal loop diagram to explore the intricate interconnections within the urban water resources system. Through simulating water supply and demand in Shenzhen from 2021 to 2035, the model identified key sensitive factors and examined various utilization scenarios for multiple water resources. Results indicated that water scarcity posed a significant obstacle to Shenzhen's development. To tackle this challenge, several effective measures should be implemented, including enhancing water conservation capabilities, developing seawater resources, promoting water reuse, optimizing the economic structure, and managing population growth. Prioritizing water conservation efforts and maximizing the utilization of seawater resources were regarded as the most impactful strategies in alleviating the water crisis. Furthermore, the relationship between water conservation capabilities and seawater utilization scale was analyzed using the SD model, contributing to the development of a comprehensive water resources management strategy. The findings from this study would provide insights into robust methods for allocating water resources, thereby enhancing sustainable water management strategies applicable to regions facing similar challenges.

Unravelling the sources contributing to the urban water supply: An isotope perspective from Ljubljana, Slovenia

In cities experiencing rapid urbanization, we must continually update our understanding of the partitioning of drinking water sources concerning its supply if it is to be managed sustainably. This need is especially crucial given the pressure on water resources arising from evolving land use patterns and climate change. For this reason, a city-wide study of stable water isotopes (δ2H and δ18O) in precipitation, surface water and groundwater across Ljubljana, Slovenia, was undertaken. The goal was to characterise the temporal dynamics of urban water cycling and trace the various sources contributing to the city’s drinking water supply. Monthly water sampling, combined with hydrogeochemical and in-situ data, permitted the identification of local precipitation and surface water contributions to its two groundwater supply aquifers. In addition, a re-examination of the mean residence times (MRT) of surface waters revealed an MRT of 3–4 years, which is much longer than previously reported. Also, changes in the contributions of surface water and precipitation to groundwater were observed compared to previous studies. These findings improve our understanding of local water partitioning and provide valuable insights for water managers addressing future urban water resource management.

A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings.