Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree

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Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran (2018, methodology): “Kişi (2015) used air temperature, wind speed, relative humidity, and solar radiation as the input parameters of an SVR model to predict evaporation in Turkey over the period 2002 to 2006, and the results are indicative of appropriate performance (RMSE = 0.597mm/day).”
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, background): “where f indicates the relationship between the climate variables (predictors) and local rainfall (predictand), w, φ and b are the m-dimensional weight vector, mapping function and bias term, respectively [56].”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “By contrast, data-driven models are designed to mathematically identify the connection between the inputs and the outputs without considering the internal processes of hydrological system (Kisi 2015; Yaseen et al. 2015).”