Water Quality Prediction Based on Multi-Task Learning

Huan Wu, Shuiping Cheng (), Kunlun Xin, Nian Ma, Jie Chen, Liang Tao and Min Gao
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Huan Wu: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Shuiping Cheng: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Kunlun Xin: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Nian Ma: T.Y.Lin International Engineering Consulting (China) Co., Ltd., Chongqing 401121, China
Jie Chen: T.Y.Lin International Engineering Consulting (China) Co., Ltd., Chongqing 401121, China
Liang Tao: T.Y.Lin International Engineering Consulting (China) Co., Ltd., Chongqing 401121, China
Min Gao: School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China

IJERPH, 2022, vol. 19, issue 15, 1-19

Abstract: Water pollution seriously endangers people’s lives and restricts the sustainable development of the economy. Water quality prediction is essential for early warning and prevention of water pollution. However, the nonlinear characteristics of water quality data make it challenging to accurately predicted by traditional methods. Recently, the methods based on deep learning can better deal with nonlinear characteristics, which improves the prediction performance. Still, they rarely consider the relationship between multiple prediction indicators of water quality. The relationship between multiple indicators is crucial for the prediction because they can provide more associated auxiliary information. To this end, we propose a prediction method based on exploring the correlation of water quality multi-indicator prediction tasks in this paper. We explore four sharing structures for the multi-indicator prediction to train the deep neural network models for constructing the highly complex nonlinear characteristics of water quality data. Experiments on the datasets of more than 120 water quality monitoring sites in China show that the proposed models outperform the state-of-the-art baselines.

Keywords: multi-task learning; water quality prediction; multiple indicator prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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