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001-es BibID:	BIBFORM103468
035-os BibID:	(cikkazonosító)10653 (WOS)000851188400001 (Scopus)85137562006
Első szerző:	Harsányi Endre (agrármérnök)
Cím:	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, Safwan Mohammed
Dátum:	2022
ISSN:	1661-7827 1660-4601
Megjegyzések:	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 (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) ( C), daily maximum mean temperature (Td-max) ( C), and daily minimum mean temperature (Td-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 (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(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 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN?MLP and ANN?RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-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.
Tárgyszavak:	Társadalomtudományok Szociológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk land degradation machine learning climate change Hungary
Megjelenés:	International Journal of Environmental Research and Public Health. - 19 : 17 (2022), p. 1-19. -
További szerzők:	Bashir, Bashar Alsilibe, Firas Moazzam, Muhammad Farhan Ul Rátonyi Tamás (1967-) (agrármérnök) Alsalman, Abdullah Széles Adrienn (1980-) (okleveles agrármérnök) Nyéki Anikó (1989-) (agrármérnök) Takács István (1977-) (terület és településfejlesztési egyetemi szakközgazdász) Mohammed Safwan (1985-) (agrármérnök)
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001-es BibID:	BIBFORM082196
035-os BibID:	(Scopus)85109010420
Első szerző:	Mohammed Safwan (agrármérnök)
Cím:	Contemporary changes of greenhouse gases emission from the agricultural sector in the EU-27 / Mohammed Safwan, Alsafadi Karam, Takács István, Harsányi Endre
Dátum:	2020
ISSN:	2474-9508
Megjegyzések:	The agricultural sector is the second contributor to the worldwide emissions of greenhouse gases (GHGs), as it is responsible for 13.5% of GHG emissions. The main aim of this research is to track GHG emission from the agricultural sector in the EU-27 between 1990 and 2016 in order to determine trends and changes of emission on a country scale. To achieve the study goal, data were collected from the Organization for Economic Co-operation and Development (OECD) website, followed by the application of the Simple Linear Regression Model (SLRM). The obtained results showed that most of the EU-27 countries witnessed a significant reduction of GHG emissions from the agricultural sector, except for Iceland and Spain. Interestingly, the highest reduction conducted by the United Kingdom was followed by Germany and France, where the reduction reached 385.27; 226.72 and 294.92 tons of CO2-equivalent per year, respectively. Thus, we can conclude that most EU countries significantly reduced GHG emissions to the atmosphere.
Tárgyszavak:	Agrártudományok Állatorvosi tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk GHG
Megjelenés:	Geology, Ecology, and Landscapes. - 4 : 4 (2020), p. 282-287. -
További szerzők:	Alsafadi, Karam Takács István (1977-) (terület és településfejlesztési egyetemi szakközgazdász) Harsányi Endre (1976-) (agrármérnök)
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00008 EFOP GINOP-2.2.1-15-2016-00001 GINOP
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