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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics  2021, Vol. 3 Issue (3): 265-277    DOI: 10.1049/csy2.12015
    
Reservoir inflow predicting model based on machine learning algorithm via multi-model fusion: A case study of Jinshuitan river basin
Reservoir inflow predicting model based on machine learning algorithm via multi-model fusion: A case study of Jinshuitan river basin
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摘要： Flood prevention and disaster mitigation have a great impact on people's lives and properties, and so it is urgent to realise high-accuracy inflow predictions for flood early warning. To this end, a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir. Firstly, a data formatting scheme called the ’hydrological regime profile‘ is designed for input data. The whole data set is partitioned into a low-flow subset and a high-flow subset. Considering the high dimensions of the complex input data, convolutional neural networks (CNN), EXtreme gradient Boosting model (XGBoost) and a partial least squares model (PLS) are used. In the CNN and XGBoost models, a special loss function weighted on inflow is designed to improve the performance on high-inflow predictions. Finally, a multi-model combination method is proposed to improve the prediction performance. Compared with XGBoost, CNN and PLS, the root mean square error of the combined model is reduced by 41.64%, 72.29% and 3.41%, respectively. As a consequence, the combined model is able to predict the inflows with higher accuracy compared to the single models.
Abstract: Flood prevention and disaster mitigation have a great impact on people's lives and properties, and so it is urgent to realise high-accuracy inflow predictions for flood early warning. To this end, a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir. Firstly, a data formatting scheme called the ’hydrological regime profile‘ is designed for input data. The whole data set is partitioned into a low-flow subset and a high-flow subset. Considering the high dimensions of the complex input data, convolutional neural networks (CNN), EXtreme gradient Boosting model (XGBoost) and a partial least squares model (PLS) are used. In the CNN and XGBoost models, a special loss function weighted on inflow is designed to improve the performance on high-inflow predictions. Finally, a multi-model combination method is proposed to improve the prediction performance. Compared with XGBoost, CNN and PLS, the root mean square error of the combined model is reduced by 41.64%, 72.29% and 3.41%, respectively. As a consequence, the combined model is able to predict the inflows with higher accuracy compared to the single models.
出版日期: 2021-07-19
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作者相关文章　　
Wei Zhang
Hanyong Wang
Yemin Lin
Jianle Jin
Wenjuan Liu
Xiaolan An

引用本文:

Wei Zhang, Hanyong Wang, Yemin Lin, Jianle Jin, Wenjuan Liu, Xiaolan An. Reservoir inflow predicting model based on machine learning algorithm via multi-model fusion: A case study of Jinshuitan river basin. IET Cyber-Systems and Robotics, 2021, 3(3): 265-277.

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https://www.zjujournals.com/iet-csr/CN/10.1049/csy2.12015    或    https://www.zjujournals.com/iet-csr/CN/Y2021/V3/I3/265

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