机器学习在水库下泄水温预测中的应用分析OA
Application of Machine Learning to Predict the Discharge Water Temperature of Reservoirs
探讨不同机器学习模型在水库下泄水温预测中的可靠性,构建高精度水温预测体系,为水库生态调度优化与水库管理提供可靠的技术支撑.以长江上游大渡河瀑布沟水电站为研究对象,采用Spearman相关系数筛选影响水库下泄水温的气象因子与水库运行参数,通过遗传算法优化随机森林(RF)、支持向量回归(SVR)、轻量级梯度提升机(LightGBM)、卷积神经网络(CNN)以及长短时记忆网络(LSTM)5种模型参数,开展下泄水温预测研究.结果表明:(1)露点温度与下泄水温相关性最高,相关系数为0.89,风速、云量、太阳辐射和坝前水位与下泄水温相关系数未能达到0.4;(2)遗传算法优化后的模型在训练集上都有较好的表现,其中RF模型表现最优,决定系数r2=0.997,LSTM表现最差(r2=0.953);(3)在下泄水温预测中,各个模型均有较好的拟合效果(决定系数r2>0.931,平均绝对误差≤0.662 ℃,均方根误差≤0.852 ℃),其中RF、LightGBM模型残差范围较小,SVR、CNN模型的最大残差更小.经遗传算法优化的机器学习方法可有效应用于水库下泄水温预测.
The operation of hydropower station significantly alters the temporal and spatial distribution of water temperature in the river channel downstream of the dam.In this study,Pubugou hydropower sta-tion on Dadu River in the upper reaches of Yangtze River was selected for research,and we explored the reliability of five machine learning models in predicting the temperature of water discharged from the res-ervoir.Our aim was to develop a method for high-precision water temperature prediction and to provide technical support for optimizing the ecological operation and management of reservoirs.The five learning models included the Random Forest(RF),Support Vector Regression(SVR),Light Gradient Boosting Machine(LGBM),Convolutional Neural Network(CNN),and Long Short-Term Memory Network(LSTM).To begin with,the Spearman correlation coefficient was used to screen meteorological factors and reservoir operation parameters that affect the temperature of water discharged from the reservoir.Then,a genetic algorithm was applied to optimize the parameters of the five models used to predict dis-charge water temperature.Results show:(1)The dew point temperature exhibited the highest correlation with the discharge water temperature,with a correlation coefficient of 0.89,while the correlation coeffi-cients between wind speed,cloud cover,solar radiation and reservoir water level with discharge water temperature were all less than 0.4.(2)The models optimized by the genetic algorithm all performed well with the training set,but the RF model gave the best results(r2=0.997),while the LSTM model gave the worst results(r2=0.953).(3)In predicting discharge water temperature,all models gave a good fit,with r2>0.931,a mean absolute error ≤0.662 ℃,and a mean square error ≤0.852 ℃.Among them,the RF and LGBM models had narrow residual ranges,and the maximum residuals of the SVR and CNN models were smaller.In conclusion,the machine learning methods optimized using the genetic algorithm can effec-tively predict the temperature of water discharged from reservoirs.
陈俊光;杨世伟;王远铭;梁瑞峰;李克锋
四川大学山区河流保护与治理全国重点实验室,四川成都 610065四川大学山区河流保护与治理全国重点实验室,四川成都 610065四川大学山区河流保护与治理全国重点实验室,四川成都 610065四川大学山区河流保护与治理全国重点实验室,四川成都 610065四川大学山区河流保护与治理全国重点实验室,四川成都 610065
建筑与水利
水库下泄水温水温预测机器学习遗传算法模型优化
reservoir discharge water temperaturewater temperature predictionmachine learninggenetic algorithmmodel optimization
《水生态学杂志》 2026 (3)
172-178,7
科技基础资源调查专项(2022FY100203)国家自然科学基金联合基金重点项目(U2240212).
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