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Article

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

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Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran
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Department of Civil Engineering, University of Zabol, Zabol 9861335856, Iran
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Department of Civil Engineering, Ilia State University, Tbilisi 0162, Georgia
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Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
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Department of Civil Engineering Science, School of Civil Engineering and the Built Environment, Kingsway Campus, University of Johannesburg, P.O. Box 524, Aukland Park, Johannesburg 2006, South Africa
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Department of Town Planning, Engineering Networks and Systems, South Ural State University (National Research University), 76, Lenin Prospekt, 454080 Chelyabinsk, Russia
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Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, ul. Norwida 25, 50-375 Wrocław, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Fangxin Fang
Water 2021, 13(17), 2451; https://doi.org/10.3390/w13172451
Received: 2 August 2021 / Revised: 1 September 2021 / Accepted: 3 September 2021 / Published: 6 September 2021
(This article belongs to the Section Hydrology)
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) < 0.77 mm and a Willmott index (d) > 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 > 0.65 at α = 0.01 and α = 0.05). View Full-Text
Keywords: pan evaporation; machine learning models; improved kriging; SVR; MARS pan evaporation; machine learning models; improved kriging; SVR; MARS
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MDPI and ACS Style

Zounemat-Kermani, M.; Keshtegar, B.; Kisi, O.; Scholz, M. Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction. Water 2021, 13, 2451. https://doi.org/10.3390/w13172451

AMA Style

Zounemat-Kermani M, Keshtegar B, Kisi O, Scholz M. Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction. Water. 2021; 13(17):2451. https://doi.org/10.3390/w13172451

Chicago/Turabian Style

Zounemat-Kermani, Mohammad, Behrooz Keshtegar, Ozgur Kisi, and Miklas Scholz. 2021. "Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction" Water 13, no. 17: 2451. https://doi.org/10.3390/w13172451

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