[1]SHAN Zhendong,LUO Han,LIU Dun.Evaporation Model Evaluation Based on Machine Learning Algorithm[J].Research of Soil and Water Conservation,2023,30(03):289-294.[doi:10.13869/j.cnki.rswc.2023.03.036]
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Evaporation Model Evaluation Based on Machine Learning Algorithm
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
30
Number of periods:
2023 03
Page number:
289-294
Column:
Public date:
2023-04-10
- Title:
- Evaporation Model Evaluation Based on Machine Learning Algorithm
- CLC:
- S161.4
- DOI:
- 10.13869/j.cnki.rswc.2023.03.036
- Abstract:
- [Objective]The optimal feature set was determined in order to explore the reasonable number of climatic factors to establish an evaporation model, based on filtering by feature selection algorithm. [Methods] 16 years(2005-01 to 2021-03)of Yulin, Jinghe and Hanzhong weather stations of hour-by-hour observation data were taken. The model parameters and the number of characteristic variables were optimized by using feature selection and traversal loop methods. Based on the best parameters combined with two machine learning algorithms, the random forest model and the multiple linear regression model, evaporation model suitable for 3 regions was established, and the three indicators of average absolute error, root mean square error and square correlation coefficient were used to evaluate the model accuracy. [Results] The number of feature variables and decision trees are 8 and 61, the prediction effect of the model is the best. The root mean square error was used to evaluate the prediction effects of the optimized random forest model and the multiple linear regression model, and the error size of the two models was 0. Except for the Jinghe area, the root mean square error of Yulin and Hanzhong was smaller than that of the optimized multiple linear regression model. Predict effects of evaporation in Yulin, Jinghe and Hanzhong fitted by the optimized random forest model are 0.85, 0.90 and 0.86, and the optimized multiple linear regression model fitting effects are 0.77, 0.83 and 0.79. The optimized random forest model of the average absolute error and the root mean square error are lower than optimized multiple linear regression model. [Conclusion] On the whole, the optimized two models have good prediction effects and the prediction effect of the random forest model is better than the multiple linear regression model.
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Last Update:
2023-04-10