Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe

Endre Harsányi, Bashar Bashir, Firas Alsilibe, Muhammad Farhan Ul Moazzam, Tamás Ratonyi, Abdullah Alsalman, Adrienn Széles, Aniko Nyeki, István Takács and Safwan Mohammed ()
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Endre Harsányi: Institute of Land Use, Technical and Precision Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary
Bashar Bashir: Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Firas Alsilibe: Department of Transport Infrastructure and Water Resources Engineering, Széchenyi István University, Egyetem tér 1, 9026 Gyor, Hungary
Muhammad Farhan Ul Moazzam: Department of Civil Engineering, College of Ocean Science, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea
Tamás Ratonyi: Institute of Land Use, Technical and Precision Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary
Abdullah Alsalman: Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Adrienn Széles: Institute of Land Use, Technical and Precision Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary
Aniko Nyeki: Department of Biosystems and Food Engineering, Faculty of Agricultural and Food Sciences, Széchenyi István University, Vár Square 2, 9200 Mosonmagyarovar, Hungary
István Takács: Doctoral School of Humanities, University of Debrecen, Egyetem Tér 1, 4032 Debrecen, Hungary
Safwan Mohammed: Institute of Land Use, Technical and Precision Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary

IJERPH, 2022, vol. 19, issue 17, 1-19

Abstract: The Modified Fournier Index ( MFI ) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation ( p i , P total ) (mm), daily maximum precipitation ( P d-max ) (mm), monthly mean temperature ( T avg ) (°C), daily maximum mean temperature ( T d-max ) (°C), and daily minimum mean temperature ( T d-min ) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good ( NSE Budapest(SC3) = 0.71, NSE Pécs(SC2) = 0.69). Additionally, the performance of RBF was accurate ( NSE Debrecen(SC4) = 0.68, NSE Pécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 ( P d-max + p i + P total ) and SC4 (P total + T avg + T d-max + T d-min ) as the best scenarios for predicting MFI by using the ANN–MLP and ANN–RBF, respectively. However, the sensitivity analysis highlighted that P total , p i , and T d-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.

Keywords: land degradation; machine learning; climate change; Hungary (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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