Abstract
Finding an accurate computational method for estimating pan evaporation (EPm) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the increasing, decreasing, and constant accuracy behavior of this hybrid model and improving the results of estimating EP compared to the two models ANN and SVM on a monthly scale of EPm in four meteorological stations (Ardabil, Khalkhal, Manjil (from Iran), and Grand Island (from the USA)) located in semi-arid regions, using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)), was evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE), and coefficient of determination (R2)) and with the help of case visual indicators were compared. According to the results of evaluation indicators in the test phase, MM-SVM-6, ANN-5, MM-SVM-3, and MM-SVM-7 with RMSE = 1.088, 0.761, 0.829, and 0.134 mm/day; MAE = 0.79, 0.54, 0.589, and 0.105 mm/day; KGE = 0.819, 0.903, 0.972, and 0.981; and R2 = 0.939, 0.962, 0.967, and 0.996 and with four input variables were introduced as the best models in Ardabil, Khalkhal, Manjil, and Grand Island stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs estimated by independent models, its power to estimate EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant, and decreasing modes in EPm estimation accuracy by this hybrid model under the semi-arid climatic conditions of the studied areas were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.
Highlights
Evaporation is known as an important hydrological process that converts liquid water to steam, and this factor, along with evapotranspiration, leads to the loss of 60% of global rainfall (Ghaemi et al 2019; Kisi 2015; Malik et al 2020b)
Indirect methods include the use of artificial intelligence (AI) algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs), which have been widely used by researchers to extract the relationship between meteorological and Ep data (Bruton et al 2000; Ghorbani et al 2013; Kişi 2006; Malik et al 2020a; Seifi and Soroush 2020)
The results of their research showed the high ability of ANN and SVM models in estimating Ep and in general, the SVM method was superior to the ANN method
Summary
Evaporation is known as an important hydrological process that converts liquid water to steam, and this factor, along with evapotranspiration, leads to the loss of 60% of global rainfall (Ghaemi et al 2019; Kisi 2015; Malik et al 2020b). It is difficult to derive a precise formula for all physical evaporation processes due to its instability, nonlinearity, and complexity To overcome these difficulties, many researchers in recent years have tried to use indirect methods to predict Ep using meteorological parameters. In a research (Ghorbani et al 2018a), scholars used a hybrid prediction model (Multilayer Perceptron-Firefly Algorithm (MLP-FFA)) based on the FFA optimizer embedded in the MLP method to predict Pan evaporation and the results of the MLPFFA hybrid model. Researchers estimated the daily EP in the semi-arid region of Iran, two methods of artificial neural network (ANN) and multivariate nonlinear regression (MNLR) were examined by determining the different combinations of climatic variables (Tabari et al 2010).
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