Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study

Abstract

Accurate water demand forecasting is essential for water utilities for effective water planning and pump scheduling. To conduct effective water planning and pump scheduling, Sydney Water needs a reliable method for predicting water demand. Short-term water demand forecasting deep learning- based models have been applied to different datasets collected from various water plants in the Greater Sydney region. This paper uses correlation analysis, the attention mechanism and a deep convolutional neural network to identify and study the importance of n number of factors that inform water prediction in the Greater Sydney region. The aim of this study is to develop, evaluate and compare multi- variate input deep learning-based models to predict water demand in various locations in the Greater Sydney region. This paper investigates five models — the recurrent neural network (RNN), long short- term memory networks (LSTM), bidirectional long short-term memory networks (BiLSTM), gated recurrent unit (GRU), and the deep convolutional neural network (CNN). The results of this study demonstrate that all the proposed models achieved a superior forecasting performance with accuracy hits of 97% using water consumption and climate factors as input data.