Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake
Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake
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混合元启发式机器学习水位预测方法--以洞庭湖为例
DOI：
10.3389/feart.2022.928052
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
B. Deng;Pan Liu;R. Chin;Pavitra Kumar;Changbo Jiang;Yifei Xiang;Yizhuang Liu;S. Lai;
中科院分区：
文献类型：
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作者：
B. Deng;Pan Liu;R. Chin;Pavitra Kumar;Changbo Jiang;Yifei Xiang;Yizhuang Liu;S. Lai;
一个可靠的水位预测在湖泊系统是至关重要的水资源管理，防洪等，本研究的目的是提出一个机器学习模型，这是能够达到相当高的精度水平的水位预测。洞庭湖是中国第二大淡水湖系，被选为研究区域。以上游站的逐时水位、流量、降雨量、气温和下游站的降雨量为输入特征，预测下游站的水位。多层感知器神经网络（MLP-NN），Elman神经网络（ENN），和集成粒子群优化算法的Elman神经网络（PSO-ENN）被选为模型开发技术。PSO-ENN模型在洞庭湖下游测站的净误差为0.929-0.988，均方根误差为0.129-0.322，平均误差为0.151-0.359，表现最好。PSO-ENN模型也显示出其能够提供更好的性能提前36 h的水位预测。在输入变量的敏感性方面，所开发的模型是最敏感的流量，其次是降雨量。
A reliable water level prediction in a lake system is crucial for water resources management, flood control, etc. The objective of this study is to propose a machine learning model which is able to achieve a considerably high level of accuracy in terms of water level prediction. Dongting Lake, which is the second-largest freshwater lake system in China, was selected as the study area. The hourly water level, flow rate, rainfall and temperature of the upstream water stations and rainfall of the downstream water stations were used as the input features, to predict the water level at the downstream stations. Multilayer perceptron neural network (MLP-NN), Elman neural network (ENN), and integration of particle swarm optimisation algorithm to Elman neural network (PSO-ENN) were selected as the model development techniques. The PSO-ENN model appears as the best performed model, as it records NSE of 0.929–0.988, RMSE of 0.129–0.322 and MAE of 0.151–0.359 at the downstream stations in Dongting Lake. The PSO-ENN model also shows its ability to provide better performance for the water level prediction of 36 h in advance. In terms of input variables sensitivity, the developed model is most sensitive to flow rate, followed by rainfall.