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- W2371417350 abstract "ET0is an important basis for computing crop water requirement,optimal allocation of regional water resources. In order to improve the prediction accuracy of ET0,the particle swarm optimization algorithm was introduced to the prediction ET0,and using the support vector regression to optimize the parameters. PSO-SVM took the maximum temperature,minimum temperature,relative humidity,wind and sunshine duration into SVM learning,the SVM parameter as the particles in the PSO,the ET0value as the target function of PSO,and then through the mutual cooperation of SVM particles obtained optimal parameters to forecast,ET0and PM model was used to calculate the value of verification.Taking Kashi area as an example, this paper got model by algorithm training with PSO-SVM;and finally,compared the results with PSO-SVM and BP neural network by predicting 10 groups samples.The results showed that PSO-SVM,which showed that the higher correlation coefficient between the predicted and measured values(0.682)and lower average relative error rate(3.19%),which were better than BP neural network." @default.
- W2371417350 created "2016-06-24" @default.
- W2371417350 creator A5041868124 @default.
- W2371417350 date "2014-01-01" @default.
- W2371417350 modified "2023-09-23" @default.
- W2371417350 title "Prediction of ET_0 Based on Particle Swarm Optimization and Support Vector Regression" @default.
- W2371417350 hasPublicationYear "2014" @default.
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