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| DOI：10.13522/j.cnki.ggps.2021600 |
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| Using Machine Learning to Predict Water Level in the Drainage Sluice Stations Following Rainfalls |
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JIANG Zewei, YANG Shihong, LIU Zhenyang, et al.
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1. College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China;
2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;
3. Cooperative Innovation Center for Water Safety & Hydro Science, Hohai University, Nanjing 210098, China;
4. Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
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| Abstract: |
| 【Objective】The aim of this paper is to propose a new method to improve the accuracy of the prediction of water level at drainage sluice stations after rainfalls.【Method】The analysis was based on field-measured water level data spanning over one year. We collected hydrological data measured from 2010—2020 from two sluice stations at Xijiakou andTianguan, respectively, in the Sihu Basin, and used two machine learning methods -support vector machine regression (SVR) and regression tree - to predict the most unfavorable conditions of the sluice stations after rainfalls. 【Result】Both SVR and the regression tree model are able to predict the maximum sluice water level after rainfalls in the two stations with R2>0.80. On average, the two models worked better for the Xijiakou station than for the Tianguan station. The selection of the kernel function has a consequence for the SVR model, with the linear kernel function working better. The regression tree model was slightly better than the SVR model.【Conclusion】The maximum sluice water level following rainfalls can be predicted reasonably well using the characteristic variables of the sluice water level, rainfall intensity, rainfall duration, and drainage flow of the pumping station. It is necessary to train the machine learning methods for different sluice stations to find the most accurate one. |
| Key words: farmland; machine learning; water level before the gate; waterlogging disaster; prediction |
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