Deep Learning Model-Based Demand Forecasting for Secondary Water Supply in Residential Communities.

Water is essential for the survival of life on Earth, and its depletion can be attributed to both natural processes and human activities. Despite the persistence of freshwater resources on our planet, the exponential growth of the global population has intensified the demand for freshwater. To ensure the sustainable use of this vital resource, effective water management and accurate forecasting are imperative. In the realm of urban water management, two critical factors come into play: water demand and population forecasting. These parameters serve as the cornerstone for developing strategies to efficiently manage urban water resources. Traditional methods of demand forecasting often struggled when dealing with historical data that was unstructured or semi-structured. However, the advent of machine learning has revolutionized the field, offering a powerful approach for forecasting. One machine learning technique that has gained prominence in this context is Long Short-Term Memory (LSTM). LSTM is a type of recurrent neural network (RNN) designed to process and forecast sequential data. It excels at capturing dependencies and patterns in time-series data, making it well-suited for water demand and forecasting.