Optimization of Support Vector Machine with Biological Heuristic Algorithms for Estimation of Daily Reference Evapotranspiration Using Limited Meteorological Data in China
Hongtao Guo | Liance Wu | Xianlong Wang | Xuguang Xing | Jing Zhang | Shunhao Qing | Xinbo Zhao
Precise estimation of daily reference crop evapotranspiration (ET<sub>0</sub>) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges for accurate ET<sub>0</sub> prediction. Here, we evaluate the performance of a support vector machine (SVM) and its hybrid models, PSO-SVM and WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify a high-precision, low-input ET<sub>0</sub> estimation tool. The findings indicate that the hybrid models, particularly WOA-SVM, demonstrated superior accuracy with R<sup>2</sup> values ranging from 0.973 to 0.999 and RMSE values between 0.123 and 0.863 mm/d, outperforming the standalone SVM model with R<sup>2</sup> values of 0.955 to 0.989 and RMSE values of 0.168 to 0.982 mm/d. The standalone SVM model showed relatively lower accuracy with R<sup>2</sup> values of 0.822 to 0.887 and RMSE values of 0.381 to 1.951 mm/d. Notably, the WOA-SVM model, with R<sup>2</sup> values of 0.990 to 0.992 and RMSE values of 0.092 to 0.160 mm/d, emerged as the top performer, showcasing the benefits of the whale optimization algorithm in enhancing SVM’s predictive capabilities. The PSO-SVM model also presented improved performance, especially in the temperate continental zone (TCZ), subtropical monsoon region (SMZ), and temperate monsoon zone (TMZ), when using limited meteorological data as the input. The study concludes that the WOA-SVM model is a promising tool for high-precision daily ET<sub>0</sub> estimation with fewer meteorological parameters across the different climatic zones of China.
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