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Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction
Copyright policy )Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction
This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) <
0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value >
0.77 mm and a Willmott index (d) >
0.65 at α = 0.01 and α = 0.05).
- Shahid Bahonar University of Kerman Iran (Islamic Republic of)
- Zabol University Iran (Islamic Republic of)
- Ilia State University Georgia
- ILIA STATE UNIVERSITY Georgia
Microsoft Academic Graph classification: Soft computing Computational model Multivariate statistics Regression Support vector machine Spline (mathematics) Kriging Conjugate gradient method Algorithm Mathematics
SVR, Water supply for domestic and industrial purposes, Geography, Planning and Development, MARS, Hydraulic engineering, Aquatic Science, improved kriging, Biochemistry, pan evaporation, machine learning models, TC1-978, TD201-500, Water Science and Technology
SVR, Water supply for domestic and industrial purposes, Geography, Planning and Development, MARS, Hydraulic engineering, Aquatic Science, improved kriging, Biochemistry, pan evaporation, machine learning models, TC1-978, TD201-500, Water Science and Technology
Microsoft Academic Graph classification: Soft computing Computational model Multivariate statistics Regression Support vector machine Spline (mathematics) Kriging Conjugate gradient method Algorithm Mathematics
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Citations provided by BIP!Popularity provided by BIP!- Shahid Bahonar University of Kerman Iran (Islamic Republic of)
- Zabol University Iran (Islamic Republic of)
- Ilia State University Georgia
- ILIA STATE UNIVERSITY Georgia
This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) <
0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value >
0.77 mm and a Willmott index (d) >
0.65 at α = 0.01 and α = 0.05).