Water Supply Prediction Based on Initialized Attention Residual Network

TL;DR

This paper introduces the Initialized Attention Residual Network (IARN), a CNN-based model that improves water supply prediction accuracy and robustness by addressing weather and holiday influences, outperforming existing methods.

Contribution

The paper proposes a novel CNN architecture with attention and residual modules for water supply forecasting, replacing RNNs to enhance reliability and generalization.

Findings

Achieves state-of-the-art accuracy on multiple datasets

Demonstrates improved robustness against weather and holiday factors

Outperforms traditional RNN-based models in water prediction tasks

Abstract

Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN)to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several data sets, in terms of accuracy, robustness and generalization ability.