Open AccessReservoir inflow predicting model based on machine learning algorithm via multi‐model fusion: A case study of Jinshuitan river basin
Author(s) -
Zhang Wei,
Wang Hanyong,
Lin Yemin,
Jin Jianle,
Liu Wenjuan,
An Xiaolan
Publication year - 2021
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/csy2.12015
Subject(s) - inflow , computer science , algorithm , flood warning , mean squared error , flood myth , boosting (machine learning) , artificial intelligence , data mining , mathematics , geology , statistics , philosophy , oceanography , theology
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.