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Background: Rapid population growth and the large number of residential developments go hand in hand with the increasing need for clean water, especially drinking water. Basically, a city's development plan is closely related to the availability of clean water in the area, because clean water is a basic human need that is very important to fulfill. However, several consumers in certain villages which are part of the PDAM North Sebatik branch pipe network service area do not receive a clean water supply. With this research, it is hoped that it can provide alternatives/solutions in solving clean water problems, especially for the Pancang Village area, North Sebatik District. Methods: The data needed in this study is data on the population of Sei Pancang Village, North Sebatik District for the last 5 years, data on PDAM customers for the last 5 years, data on clean water needs, data on clean water sources and reservoir volume, as well as map data on the clean water distribution network. The evaluation technique used in this study uses the method with the largest correlation calculation, namely the arithmetic method. The clean water distribution network map will be evaluated using epanet software. Findings: The results of this research are that the population of Sei Pancang Village, North Sebatik District in 2022 will be 5250 people and projections for the number of residents and customers for the next 10 years show that the population will increase to 7018 people. Conclusion: Water use in the development plan year, namely 2023, in the residential area of Sei Pancang Village, North Sebatik District, includes, among other things, an average daily need of 9,045 lt/s, a maximum daily need of 10,402 lt/s, and a peak hour need of 13,568 lt/s. Meanwhile, in 2032 water demand will increase, with an average daily demand of 11,697 lt/second, a maximum daily demand of 13,451 lt/second, and a peak hour demand of 20,177 lt/second. Novelty/Originality of this article: This study develops a predictive model integrating demographic, climate, and infrastructure data to project water demand and optimize distribution. This model can be applied across regions to improve water management efficiency.
Refinaldo et al. (Wed,) studied this question.