Industrial water withdrawal prediction using multi-head attention encoder model

Abstract

ABSTRACT Industrial water withdrawal prediction is the cornerstone of water resources monitoring and early warning, as well as a key task in implementing rigid constraints on water resources. Although many factors may influence water withdrawal, they are difficult to obtain. Extracting the features in the historical water withdrawal data itself is a more convenient alternative way to achieve accurate prediction. However, due to the lack of significant regularity and periodicity in industrial water data, feature extraction is challenging. In this paper, a multi-head attention encoder (MAEN) model consisting of multi-head attention mechanisms and feed-forward neural networks is proposed to enable more convenient and accurate forecasting. The multi-head attention mechanism allows to focus on different parts of the data simultaneously, detecting complex dependencies, and extracting key features from historical data to enable more efficient feature extraction. Applied to water withdrawal data from real factories, the proposed model outperforms common time series prediction models including artificial neural network, long short-term memory, and gate recurrent unit. Compared to the best-performing comparison model, MAEN shows a reduction of mean squared error by 3.2% for 1 day ahead predictions, and 8.9% for 7 day ahead predictions, illustrating its superior performance in industrial water withdrawal prediction.