Estimation Model of Pan Evaporation Based on Machine Learning Technology
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Abstract:
In order to make up for the lack of evaporation data after the stop of evaporation pan manual observations at the National Meteorological Observatory, three regional datasets of the northern Shaanxi, Guanzhong and southern Shaanxi and three single station datasets of Yulin, Jinghe and Hanzhong are established. By establishing and optimizing the KNN (KNearest Neighbor method) and MLP (MultiLayer Perceptron) models and its parameters, the regional estimation model of evaporation and the single station estimation model are constructed and verified respectively. The results show that: (1) While estimating the regional evaporation, the KNN model shows good generalization performance, and the average Mean Square Error, Total Relative Error and Correct Rate values are 0.42 and 2.1%, 57.0%, respectively; the generalization performance of the MLP model in the northern Shaanxi is poor. (2) While estimating the evaporation of a single station, the performance of the single station estimation model based on the Knearest neighbor method is superior to the regional estimation model, and the average Mean Square Error and Correct Rate index values are 0.48 and 55.0%, the absolute average value of Total Relative Error at Yulin and Jinghe 1.6%, and that at Hanzhong is relatively high, reaching 10.3%. This research provides a tool based on the Machine Learning for the estimation of daily, monthly, seasonal and annual evaporation in different climate regions and single stations and the quality control of daily evaporation data.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.