A Modified Hydrologic Model Algorithm Based on Integrating Graph Theory and GIS Database

Abstract

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.

Improved EPANET Hydraulic Model with Optimized Roughness Coefficient using Genetic Algorithm

“…This study aims to collaborate with an EPANET-based hydraulic model algorithm (Shiu et al, 2022), which is a useful and accessible tool for users to build automated processes and handle critical system parameters, such as nodes, links, demand, properties, pumps, reservoirs, and roughness. The study presents a modified approach for calibrating the roughness coefficient in a hydraulic model using a Graphic User Interface (GUI) and a Genetic Algorithm (GA).…”

“…According to suggestion of Shiu et al (2022), the total measured points of this study is set to 140 to satisfy the measured quantity which is at least 30% of the model length(km) as following Equation (3):…”

Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients

“…The roughness coefficient C of a pipe is a dimensionless number that depends on the pipe material, and in this study, the pipe roughness is categorized based on the fabrication material, including cast iron, plastic, and stainless steel. Based on the suggestion of Shiu et al (2022), the roughness of new cast iron pipes is 130, while the roughness of 20-year-old pipes is 95, and 30-year-old pipes are 82.5. For plastic pipes, regardless of the age, the roughness is set at 150.…”

“…The Water Intelligent System of Enterprise (WISE) platform of the Taipei Water Department is currently in use, which allows users to select an area on the map and choose the water demand distribution model, then export the model for further analysis by Shiu et al (2022). The resulting model can be opened in EPANET for analysis, as demonstrated in Figure 6.…”

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

“…Hierarchical analysis in combination with the fuzzy Technique for Order of Preference is used in this decision-making model. Another water resource management model is presented in the paper [57] using the GIS database and graph theory by modifying the GIS graph data and equipment management system. Seasonal variations of land use land cover and land surface temperature(LST) and the association of LULC with LST are explored and presented in [58].…”