RESEARCH ON DAM INFLOW PREDICTION DURING SEVERE FLOOD USING MACHINE LEARNING METHODS
RESEARCH ON DAM INFLOW PREDICTION DURING SEVERE FLOOD USING MACHINE LEARNING METHODS
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发表时间：
2020
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通讯作者：
M. Nakatsugawa
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作者：
M. Nakatsugawa
我们检查了机器学习方法，以确定在严重洪水期间，哪种方法最适合预测大坝和水库水位的流入。使用机器学习对石川河支流索拉奇河上游的Kanayama大坝和东京河支流Satsunaigawa大坝上游的Satsunaigawa大坝进行预测。这些预测是利用2016年8月暴雨灾害时收集的这两条河流流域的水文信息进行的。作为基于机器学习的预测方法，研究了随机森林(RF)、完全连接神经网络(FCNN)、递归神经网络(RNN)和基于稀疏建模的弹性网络回归分析。结果，FCNN和弹性网络显示了大致相同的准确性，纳什-萨克利夫(NS)系数为0.7或更高。对于弹性网络，对于非预测降雨量具有不确定性的情况，预测结果最准确，然后NS系数为0.7或更大。我们相信，所获得的结果将为改善大坝运行以减轻洪水灾害提供保证。
We examined machine learning methods to determine which one would be optimal for predicting inflow during severe flood to a dam and reservoir water level. Predictions using machine learning were done for Kanayama Dam, at the upper reaches of the Sorachi River, which is a tributary of the Ishikari River, and for Satsunaigawa Dam, at the upper reaches of the Satsunai River, which is a tributary of the Tokachi River. The predictions were done by using hydrological information for the basins of these two rivers collected at the heavy rainfall disaster of August 2016. As machine learning based prediction methods, RF (Random Forest), FCNN (Fully Connected Neural Network), RNN (Recurrent Neural Network) and regression analysis by Elastic Net, which is a sort of sparse modeling, were examined. As a result, FCNN and Elastic Net demonstrated roughly the same accuracy, with Nash-Sutcliffe (NS) coefficients of 0.7 or greater. With Elastic Net, for cases other than those whose predicted rainfall had indeterminacy, the results were the most accurate and then the NS coefficients were 0.7 or greater. We are sure that obtained results give a promise to improve dam operation for disaster mitigation due to flood.