水流
支持向量机
均方误差
计算机科学
模拟退火
基因表达程序设计
人工智能
算法
机器学习
数据挖掘
数学
统计
流域
地理
地图学
作者
Rana Muhammad Adnan,Özgür Kisi,Reham R. Mostafa,Ali Najah Ahmed,Ahmed El-Shafie
标识
DOI:10.1080/02626667.2021.2012182
摘要
This paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage.
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