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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:BIBFORM105967
035-os BibID:(cikkazonosító)16469 (WOS)000902497100001 (Scopus)85144544776
Első szerző:Nxumalo, Gift
Cím:Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa / Gift Nxumalo, Bashar Bashir, Karam Alsafadi, Hussein Bachir, Endre Harsányi, Sana Arshad, Safwan Mohammed
Dátum:2022
ISSN:1661-7827 1660-4601
Megjegyzések:Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002-2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann-Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002-2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr-Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
water
meteorological drought
crop yield
food security
land
climate change
South Africa
Megjelenés:International Journal of Environmental Research and Public Health. - 19 : 24 (2022), p. 1-22. -
További szerzők:Bashir, Bashar Alsafadi, Karam Bachir, Hussein Harsányi Endre (1976-) (agrármérnök) Arshad, Sana Mohammed Safwan (1985-) (agrármérnök)
Pályázati támogatás:NKFIA-TKP2021-NKTA-32
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