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Water Quality Prediction Based on Multi-Task Learning

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  • Huan Wu

    (College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
    T.Y.Lin International Engineering Consulting (China) Co., Ltd., Chongqing 401121, 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
    Faculty of Natural Sciences, University of the Western Cape, Cape Town 7535, South Africa)

  • Jie Chen

    (T.Y.Lin International Engineering Consulting (China) Co., Ltd., Chongqing 401121, China
    College of Environment and Ecology, Chongqing University, Chongqing 400030, 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)

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.

Suggested Citation

  • Huan Wu & Shuiping Cheng & Kunlun Xin & Nian Ma & Jie Chen & Liang Tao & Min Gao, 2022. "Water Quality Prediction Based on Multi-Task Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9699-:d:881965
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    References listed on IDEAS

    as
    1. Lindbeck, Assar & Snower, Dennis J, 2000. "Multitask Learning and the Reorganization of Work: From Tayloristic to Holistic Organization," Journal of Labor Economics, University of Chicago Press, vol. 18(3), pages 353-376, July.
    2. Essam A. Rashed & Akimasa Hirata, 2021. "Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling," IJERPH, MDPI, vol. 18(15), pages 1-15, July.
    3. Mehwish Dildar & Shumaila Akram & Muhammad Irfan & Hikmat Ullah Khan & Muhammad Ramzan & Abdur Rehman Mahmood & Soliman Ayed Alsaiari & Abdul Hakeem M Saeed & Mohammed Olaythah Alraddadi & Mater Husse, 2021. "Skin Cancer Detection: A Review Using Deep Learning Techniques," IJERPH, MDPI, vol. 18(10), pages 1-22, May.
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    Cited by:

    1. Jesmeen Mohd Zebaral Hoque & Nor Azlina Ab. Aziz & Salem Alelyani & Mohamed Mohana & Maruf Hosain, 2022. "Improving Water Quality Index Prediction Using Regression Learning Models," IJERPH, MDPI, vol. 19(20), pages 1-23, October.

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