| Cite this article: | 江赜伟,杨士红,柳真杨,等.基于机器学习的排涝闸站雨后水位预测[J].灌溉排水学报,2022,():-. |
| JIANG Zewei,YANG Shihong,LIU Zhenyang,et al.基于机器学习的排涝闸站雨后水位预测[J].灌溉排水学报,2022,():-. |
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| This article has been：Browse 1527Times Download 0Times |
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| DOI：10.13522/j.cnki.ggps.2021600 |
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| Prediction of water level of drainage sluice station after rain based on machine learning |
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JIANG Zewei1, YANG Shihong1, LIU Zhenyang1, XU Junzeng1, Pang Qingqing2
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1.College of Agricultural Science and Engineering,Hohai University;2.Nanjing Institute of Environmental Sciences,Ministry of Ecology and Environment
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| Abstract: |
| 【Objective】 The prediction of water level after rain at the drainage sluice station has a certain reference significance for regulating the adverse working conditions of the sluice station and early warning of farmland waterlogging. However, there is no simple and feasible method at present. 【Method】 Based on the analysis of one year"s field measured water level data, this study collected the hydrological data of two typical sluice stations (Xijiakou station and Tianguan station) in the Sihu basin in the past ten years (2010-2020) and used two machine learning methods (support vector machine regression SVR and regression tree) to predict the most unfavorable conditions of sluice station after rain. 【Result】 The results showed that SVR and regression tree model could well simulate the maximum sluice water level after the rain of Xijiakou station and Tianguan station, R2 was basically greater than 0.80; the prediction performance of the two machine learning models in Xijiakou station was better than that in Tianguan station. The selection of kernel function has a certain influence on the prediction results of the SVR model, and the linear kernel function is more stable. The effect of the regression tree model was slightly better than the SVR model. 【Conclusion】 It is feasible to predict the maximum sluice water level after the rain by using the characteristic variables of sluice water level, rainfall, rainfall time, and drainage flow of pumping station. Moreover, it is necessary to train different sluice stations separately and find the optimal machine learning algorithm. In the future, it is necessary to realize the real-time prediction of farmland waterlogging by combining it with rainfall forecast data. This study has certain guidance and reference significance in the regulation of water conservancy facilities and the management of farmland waterlogging prevention and mitigation in waterlogging prone areas. |
| Key words: Farmland, machine learning, water level before the gate, waterlogging disaster; prediction |
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