Integrated Deep Learning Framework for Water Demand Forecasting and Leakage Detection

Abstract Water scarcity and inefficient distribution are major challenges in modern urban water management systems. Accurate water demand forecasting and early leakage detection are essential for ensuring sustainable water resource utilization. This research proposes an integrated deep learning framework for water demand forecasting and leakage detection using advanced time-series prediction models. In the first phase, a forecasting model based on Long Short-Term Memory (LSTM) is created to estimate daily water usage by analyzing past consumption data. The dataset, which includes around 4700 daily records, was processed using normalization and sequence generation methods. The suggested LSTM model was trained with the Adam optimizer and mean squared error loss to identify temporal consumption patterns. The experimental results indicate that the model accurately tracks real consumption patterns, achieving a Root Mean Square Error (RMSE) of 93663, which highlights the effectiveness of deep learning in predicting urban water demand. In the next phase of the research, additional parameters such as flow, pressure, and environmental factors will be incorporated to perform leakage detection, and both forecasting and leakage modules will be integrated into a unified deep learning framework for smart water management. The proposed approach provides a scalable and intelligent solution for sustainable water distribution and can support smart city water management systems.