Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Abstract

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

“…Wen et al (2015),Mehdizadeh et al (2017), andAdnan et al (2021) confirm that, overall, the soft computing approach performs better than empirical equations, which is in agreement with the present study finding. Furthermore,Mehdizadeh et al (2017) reported the strong ability of SVM-polynomial in estimating monthly ETo in Iran.…”

“…The GMDH-NN model has the ability to handle several inputs variables and predict a single output, using different layers in the model. Each layer output considers an input to the next layer (Adnan et al 2021). The GMDH-NN's layer structure can be presented as:…”

“…Thus, several alternative empirical equations are used that require fewer climate records. The performance of the alternative equations depends mainly on climate characteristic patterns which have a significant impact on the estimations (Adnan et al 2021). Besides, soft computing techniques based on artificial intelligence (AI) algorithms have been widely used to reliably estimate ETo from available and recorded meteorological variables (Alizamir et al 2020).…”

“…Salam et al (2020) used 13 empirical equations and Random Forest (RF) algorithm to estimate monthly ETo in Bangladesh and found that the Abtew (1996) model has the closest estimates and the best linear correlation with FAO56-PM. Recently, Adnan et al (2021) proved the feasibility of the group method of data handling neural network (GMDH-NN) which used temperatures records as a predictor and found its high performance in Turkey compared to MARS and other empirical equations.…”

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

“…In the advent of soft computing tools, developments in data-driven models using metaheuristic algorithms have been incorporated to model various hydrological processes. The most common meta-heuristic algorithms are support vector machine (SVM) [2,8,10], random tree (RT), artificial neural networks (ANNs), M5 pruning tree (M5P) [2,16,17], reduced error pruning tree (REPTree), multivariate adaptive regression splines (MARS) [2,13,16], extreme learning machine (ELM), gene expression programming (GEP), and random subspace (RSS). Furthermore, their hybrids with a variety of algorithms have been efficiently used to estimate pan evaporation [14,18,19].…”