Short-Term Urban Water Demand Forecasting Using an Improved NeuralProphet Model

The use of machine learning models for short-term network flow prediction has become increasingly widespread in recent years. Existing data-driven models are usually able to achieve good accuracy, but machine learning models are usually weakly interpretable and cannot provide clear decision guidance to decision makers in practical applications. Determining the input data shape of the model has an important impact on improving the interpretability of the model and understanding the relationship between the input factors and the application scenarios in the case. In this study, we used an integrated model for urban water demand prediction, which is based on the NeuralProphet model, and introduced the MIC method to screen the model input factors, which led to improvements in the accuracy of the prediction model. The aim of this work is also to improve the interpretability of water demand forecasting methodologies and the applicability of this model in the context of climate change and the complexity of urban water management, in order to help water managers make optimal water resource allocation decisions under different future scenarios.