APPLICATION OF MACHINE LEARNING MODELS FOR ESTIMATING AND PREDICTING WATER DEMAND FOR WATER DISTRIBUTION SYSTEM USING NDDC HOSTEL AS A CASE STUDY

ABSTRACT

Water is a vital resource that needs to be managed efficiently, especially in shared residential settings like hostels The NDDC Hostel experiences fluctuating water demand due to varying occupancy, daily routines, and seasonal changes. Traditional water supply systems often struggle to adapt swiftly to these dynamic patterns, leading to wastage, shortages, and operational inefficiencies. This project aims to develop a machine learning-based system that can estimate and predict water demand for water distribution system in the NDDC Hostel. This system can improve water management strategies, enhance resource allocation, and ensure an efficient and sustainable water supply. The project methodology involves collecting and preprocessing data, selecting relevant features, choosing suitable machine learning models, training and evaluating predictive models. The project uses regression algorithms and time series forecasting techniques to create accurate predictive models for water demand. The result of the project is a robust machine learning-based system that can accurately predict water demand and optimize water distribution in the NDDC Hostel. It represents a step towards smarter, data-driven water management in shared residential settings. system can reduce water wastage and promote the efficient use of water. The project demonstrates the potential of machine learning in addressing real-world challenges and advancing resource