向家坝水电站泄洪雾化对气温影响程度研究

基于向家坝蓄水后非泄洪时段的气象观测数据，使用Scikit-learn机器学习算法（K近邻回归、线性回归、决策树回归、线性SVR回归和人工神经网络），通过样本训练和交叉验证，建立向家坝水电站坝区各气象站气温预测模型，应用该模型从时空变化和影响程度等方面定量分析泄洪雾化对坝后区域气温的影响。结果表明：坝后区域的气温受向家坝水电站泄洪影响较小，且影响程度随着与泄洪孔口的距离增加而迅速减小；距离泄洪孔口最近的江边气象站在每日12~18时受泄洪雾化的影响程度最大，且在13时达到顶峰，气温影响值主要在−2.0℃以内。

Study on the Impact of Flood Discharge Atomization on Local Temperature in Xiangjiaba Hydropower Station

Based on the meteorological observation data in non-flood discharge period after Xiangjiaba impoundment, the scikit-learn machine learning algorithm (K neighbor regression, linear regression, decision tree regressoin, linear SVR regression and artificial neural network) was used to establish the temperature prediction model (TPM) of each meteorological station in the dam area of Xiangjiaba hydropower station through sample training and cross validation. The model was applied to quantitatively analyze the influence of flood discharge atomization on the temperarture of the region behind the dam from the aspeccts of temporal and spatial variation and influence degree. The results show that the temperature behind the dam is less affected by the flood discharge of Xiangjiaba Hydropower Station, and the influence degree decreases rapidly with the increase of the distance from the flood discharge orifice. The riverside weather station nearest to the flood discharge outlet is most affected by the flood discharge atomization at 12~18 o 'clock daily, and reaches its peak at 13 o 'clock, and the temperature influence value is mainly within-2.0 °C.