基于KNN-TCN模型的蒸发皿蒸发量预测研究

Prediction of Pan Evaporation Based on KNN⁃TCN Model

蒸发量的精确预测对合理开发利用水资源、旱涝变化趋势研究和农作物灌溉用水量的估算具有十分重要的意义。选取我国北方地区14个地面国际交换站观测的7项气象数据,以时间卷积网络(TCN)模型为基础模型,运用K-近邻(KNN)算法对蒸发皿蒸发量的空间因素进行筛选,构建KNN-TCN蒸发皿蒸发量预测模型,并利用平均绝对误差、均方根误差和判定系数3项指标对目标站点的蒸发量预测精度进行评价。结果表明:1)KNN-TCN模型预测结果明显优于LSTM模型;2)相比基础TCN模型,KNN-TCN模型预测结果的判定系数提升了2.52%,平均绝对误差、均方根误差分别降低了23.97%、13.06%。

Accurate prediction of evaporation is of great significance for the rational development and utilization of water resources,the study of drought and flood trends,and the estimation of crop irrigation water consumption.In this paper,7 meteorological data observed by 14 ground international exchange stations in northern China were selected.Based on the time convolution network(TCN)model,the K-nearest neighbor(KNN)algorithm was used to screen the spatial factors of pan evaporation.The KNN-TCN pan evaporation prediction model was built and the average absolute error,root mean square error and coefficient of determination were used to evaluate the evaporation prediction ac-curacy of the target site.The results show that a)the prediction results of KNN-TCN model are significantly better than that of LSTM model.b)Compared with the basic TCN model,the determination coefficient of KNN-TCN model is increased by 2.52%,and the mean absolute error and root mean square error are reduced by 23.97%and 13.06%,respectively.