Forecasting Evaporative Loss by Least-Square Support-Vector Regression and Evaluation with Genetic Programming, Gaussian Process, and Minimax Probability Machine Regression: Case Study of Brisbane…

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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): “The modelling stages were operated according to the procedures in our previous studies, e.g., Deo and Samui (2017); Deo et al. (2016, 2017b); Deo and Şahin (2015, 2016).”
Evaporation modelling by heuristic regression approaches using only temperature data (2019, methodology): “Deo and Samui (2017) applied four data-driven models – LSSVM, Gaussian process regression (GPR), minimax probability machine regression (MPMR) and genetic programming (GP) – for predicting daily Epan using several climatic variables, e.g. precipitation (P), U, mean solar exposure (St), Tmin, Tmax…”
Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran (2018, background): “…of AIbased models for the prediction of pan evaporation (Abudu et al. 2011; Bruton et al. 2000; Deo and Şahin 2015; Dogan et al. 2010; Goyal et al. 2014; Keshtegar et al. 2016; Keskin et al. 2009; Kişi 2006; Kisi et al. 2016; Macfarlane and Ogden 2012; Trajkovic 2005; Deo and Samui 2017).”