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A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model
Theoretical and Applied Climatology ( IF 3.4 ) Pub Date : 2020-07-13 , DOI: 10.1007/s00704-020-03283-4
Yiqing Guan , Babak Mohammadi , Quoc Bao Pham , S. Adarsh , Khaled S. Balkhair , Khalil Ur Rahman , Nguyen Thi Thuy Linh , Doan Quang Tri

Evaporation is one of the vital components of hydrological cycle. Precise estimation of pan evaporation (Epan) is essential for the sustainable water resources management. The current study proposed a novel approach to estimate daily Epan across the humid region of Iran using support vector regression (SVR) technique coupled with Krill Herd Algorithm (SVR-KHA). Meteorological data were collected from three stations (Bandar Abbas, Rudsar, and Osku) over a period from 2008 to 2018 and used for application. Of the data, 70% were used for training and remaining 30% were used for testing. The study considered seven different combinations of input variables for predicting daily Epan at each station. The influence of KHA hybridization is examined by comparing results of SVR-KHA algorithm with simple SVR through a multitude of statistical performance evaluation criteria such as coefficient of determination (R2), Wilmot’s index (WI), root-mean-square error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Mean Absolute Relative Error (MARE), and several graphical tools. Single input SVR1 model hybrid with KHA (SVR-KHA1) showed improved performance (R2 of 0.717 and RMSE of 1.032 mm/day) as compared with multi-input SVR models, e.g., SVR5 (with RMSE and MAE of 1.037 mm/day and 0.773 mm/day), while SVR7 model hybridized with KHA (SVR-KHA7), which considers seven meteorological variables as input, performed best as compared with other models considered in this study. Epan estimates at Bandar Abbas and Rudsar by SVR and SVR-KHA are similar (with R2 statistics values of 0.82 and 0.84 at Bandar Abbas station, and 0.88 and 0.9 at Rudsar station, respectively). However, better improvements in Epan estimates are observed at Osku station (with R2 of 0.91 and 0.86, respectively), which is situated at interior geographical location with a higher altitude than the other two coastal stations. Overall, the results showed consistent performance of SVR-KHA model with stable residuals of lower magnitude as compared with standalone SVR models.



中文翻译:

支持向量回归与磷虾畜群算法模型相结合的伊朗沿海地区日蒸发量预测的新方法

蒸发是水文循环的重要组成部分之一。锅蒸发量(E pan)的精确估算对于可持续水资源管理至关重要。当前的研究提出了一种新颖的方法,该方法使用支持向量回归(SVR)技术和Krill Herd算法(SVR-KHA)来估算伊朗潮湿地区的日平均E pan。在2008年至2018年期间从三个站点(班达阿巴斯,Rudsar和Osku)收集了气象数据,并将其用于应用。在数据中,有70%用于训练,其余30%用于测试。该研究考虑了输入变量的七个不同组合,以预测每日E Pan在每个车站。通过多种统计性能评估标准,例如确定系数(R 2),威尔莫特指数(WI),均方根误差(RMSE),将SVR-KHA算法与简单SVR的结果进行比较,从而检验KHA杂交的影响),平均绝对误差(MAE),相对均方根误差(RRMSE),平均绝对相对误差(MARE)和一些图形工具。具有KHA(SVR-KHA1)的单输入SVR1模型混合动力表现出改进的性能(R 2与多输入SVR型号(例如SVR5(RMSE和MAE分别为1.037 mm /天和0.773 mm /天)相比,SVR7为0.717,RMSE为1.032 mm /天),而SVR7模型与KHA(SVR-KHA7)杂交,将七个气象变量作为输入,与本研究中考虑的其他模型相比,效果最佳。SVR和SVR-KHA在阿巴斯港和Rudsar的E pan估算值是相似的(阿巴斯港站的R 2统计值分别为0.82和0.84,Rudsar站的R 2统计值分别为0.88和0.9)。但是,在电子商务更好的改进概算在奥斯库站(观察到,其中R 2分别为0.91和0.86),它位于内部地理位置,海拔高于其他两个沿海站点。总体而言,结果表明,与独立SVR模型相比,SVR-KHA模型具有一致的性能,且具有较低幅度的稳定残差。

更新日期:2020-07-14
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