Ensuring high quantity and quality of water for humans is becoming more important because of the water supply risks in extreme climates. With increasing urbanization, urban water resource management is becoming increasingly important. The hydrologic analysis of water supply pipelines can help decision-makers understand water pressure, flow rate, water quality, and possible leakages, extending feasible strategies for sustainable development and smart cities. In this study, an improved urban hydrologic analysis model was built by integrating the connectivity of graph theory and the geographic information system (GIS) database. The Neihu Division of the Taipei Water Department in Taiwan was taken as an example to explain the proposed process and method, and 15,131 confluence data items were used to analyze the differences between the proposed method and WaterGEMS. The results show that of the total head parameters, 72% had zero differences, 28% had a difference of less than 1 m, and about 99% of the confluences had a water pressure difference of less than 1 m. The comparison of 120 on-site water pressure measurements showed that about 85% of the confluences had an error of less than 20%. The above results demonstrated the applicability of the proposed method for water resource management on similar scales and its benefit for the development of smart cities.

The process of calibrating hydraulic models for water distribution systems (WDS) is crucial during the model-building process, particularly when determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study proposes a single-objective procedure that utilizes Genetic Algorithm (GA) for optimizing roughness coefficients in the EPANET hydraulic model. EPANET-GA incorporates an automated calibration process and a User Graphic Interface (GUI) to analyze the water head pressures of WDS nodes. Notably, the proposed method not only optimizes roughness coefficients based on pipe material but also spatial characteristics of pipes. To demonstrate the effectiveness of this method, the study builds a hydraulic analysis model for the Zhonghe and Yonghe district of the Taipei Water Department, integrating graph theory’s connectivity and the GIS database. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results show that the optimized roughness coefficient produces a high correlation coefficient (0.9) with the measured data in a certain time slot. Furthermore, a low standard error (8.93%) was acheived compared to 24-hour monitoring data. The proposed method was further compared to WaterGEMs, and the study concludes that the proposed model provides a reliable reference for the design and routing scenario of WDS.

The process of calibrating hydraulic models for water distribution systems (WDS) is crucial during the model-building process, particularly when determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study proposes a single-objective procedure that utilizes Genetic Algorithm (GA) for optimizing roughness coefficients in the EPANET hydraulic model. EPANET-GA incorporates an automated calibration process and a User Graphic Interface (GUI) to analyze the water head pressures of WDS nodes. Notably, the proposed method not only optimizes roughness coefficients based on pipe material but also spatial characteristics of pipes. To demonstrate the effectiveness of this method, the study builds a hydraulic analysis model for the Zhonghe and Yonghe district of the Taipei Water Department, integrating graph theory’s connectivity and the GIS database. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results show that the optimized roughness coefficient produces a high correlation coefficient (0.9) with the measured data in a certain time slot. Furthermore, a low standard error (8.93%) was acheived compared to 24-hour monitoring data. The proposed method was further compared to WaterGEMs, and the study concludes that the proposed model provides a reliable reference for the design and routing scenario of WDS.

Calibrating hydraulic models for water distribution systems (WDS) is crucial during model-building, particularly in determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study utilized genetic algorithm (GA) for optimizing roughness coefficients with the Environmental Protection Agency Network Evaluation Tool (EPANET) hydraulic model. EPANET-GA further considers the spatial characteristics of pipes. The study incorporated an automated calibration process and a user graphic interface to analyze the water head pressures of WDS nodes for the Zhonghe and Yonghe districts. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results reveal that the optimized roughness coefficient produces a high correlation coefficient (0.90) with the measured data in a time slot. Besides, a low standard error (8.93%) was achieved for 24-hour predictions. Furthermore, in the Shelin–Beitou district, the consideration of spatial characteristics was incorporated as constraints during the calibration process. The improved outcomes indicate that the EPANET-GA is a reliable reference for WDS design and routing scenarios in practice.