Urban Water Demand Prediction System Using LSTM

Urban water management systems are at an increased difficulty due to rapid urbanization, increasing population and climate change. Predicting water demand accurately is essential to maintaining the sustainability of water resource usage, particularly in densely populated urban regions. In this work, we present a unique approach to urban water demand forecast using predictive modeling and deep learning, taking into account demographic aspects like population growth, meteorological variables like temperature and rainfall, and historical water use data. The main model employed in this work is LSTM (Long Short Term Memory), which is utilized for time-series prediction since it can learn dependencies between multiple observations in data. Along with future water demand model, This model identifies prospective shortages by contrasting the forecasted demand against readily available amount of water. This research highlights the need of visualizing demand projections to support decision-making in deploying resources and rapidly respond in a crisis related with water shortages. Implementation of these predictive models in water management strategies may also lead to efficient resource allocation or waste minimization and sustainable use of water resources aiding the long-term resilience and sustainability of urban environments.