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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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2.

001-es BibID:BIBFORM097277
035-os BibID:(cikkazonosító)1339
Első szerző:Harsányi Endre (agrármérnök)
Cím:Impact of Agricultural Drought on Sunflower Production across Hungary / Endre Harsányi, Bashar Bashir, Firas Alsilibe, Karam Alsafadi, Abdullah Alsalman, Adrienn Széles, Muhammad Habib ur Rahman, István Bácskai, Csaba Juhász, Tamás Ratonyi, Safwan Mohammed
Dátum:2021
ISSN:2073-4433 2073-4433
Megjegyzések:In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CAR BATCLIM database (1961?2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as ?3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Atmosphere. - 12 : 10 (2021), p. 1-18. -
További szerzők:Bashir, Bashar Alsilibe, Firas Alsafadi, Karam Alsalman, Abdullah Széles Adrienn (1980-) (okleveles agrármérnök) Rahman, Muhammad Habib ur Bacskai István Juhász Csaba (1962-) (környezetgazdálkodási agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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3.

001-es BibID:BIBFORM119276
035-os BibID:(cikkazonosító)130968 (Scopus)85187128031
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900-2100) / Safwan Mohammed, Sana Arshad, Firas Alsilibe, Muhammad Farhan Ul Moazzam, Bashar Bashir, Foyez Ahmed Prodhan, Abdullah Alsalman, Attila Vad, Tamás Ratonyi, Endre Harsányi
Dátum:2024
ISSN:0022-1694
Megjegyzések:Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events is directly related to the strategic water management in the agricultural sector. The application of machine learning (ML) algorithms in different scenarios of climatic variables is a new approach that needs to be evaluated. In this context, the current research aims to forecast short-term drought i.e., SPI-3 from different climatic predictors under historical (1901-2020) and future (2021-2100) climatic scenarios employing machine learning (bagging (BG), random forest (RF), decision table (DT), and M5P) algorithms in Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Hungary), and Szombathely (SzO) (west Hungary) were selected to forecast short-term agriculture drought i.e., Standardized Precipitation Index (SPI-3) in the long run. For this purpose, the ensemble means of three global circulation models GCMs from CMIP6 are being used to get the projected (2021-2100) time series of climatic indicators (i.e., rainfall R, mean temperature T, maximum tem- perature Tmax, and minimum temperature Tmin under two scenarios of socioeconomic pathways (SSP2-4.5 and SSP4-6.0). The results of this study revealed more severe to extreme drought events in past decades, which are projected to increase in the near future (2021-2040). Man-Kendall test (Tau) along with Sen`s slope (SS) also revealed an increasing trend of SPI-3 drought in the historical period with Tau = 0.2, SS = 0.05, and near future with Tau = 0.12, SS = 0.09 in SSP2-4.5 and Tau = 0.1, SS = 0.08 in SSP4-6.0. Implementation of ML algorithms in three scenarios: SC1 (R + T + Tmax + Tmin), SC2 (R), and SC3 (R + T)) at the BD station revealed RF-SC3 with the lowest RMSE RFSC3-TR = 0.33, and the highest NSE RFSC3-TR = 0.89 performed best for forecasting SPI-3 on historical dataset. Hence, the best selected RF-SC3 was implemented on the remaining two stations (SZ and SzO) to forecast SPI-3 from 1901 to 2100 under SSP2-4.5 and SSP4-6.0. Interestingly, RF-SC3 forecasted the SPI-3 under SSP2-4.5, with the lowest RMSE = 0.34 and NSE = 0.88 at SZ and RMSE = 0.34 and NSE = 0.87 at SzO station for SSP2-4.5. Hence, our research findings recommend using SSP2-4.5, to provide more accurate drought predictions from R + T for future projections. This could foster a gradual shift towards sustainability and improve water management resources. However, concrete strategic plans are still needed to mitigate the negative impacts of the projected extreme drought events in 2028, 2030, 2031, and 2034. Finally, the validation of RF for short-term drought prediction on a large historical dataset makes it significant for use in other drought studies and facilitates decision making for future disaster management strategies.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Standardized precipitation index
Forecasting
CMIP6
Random Forest
Hungary
Megjelenés:Journal Of Hydrology. - 633 (2024), p. 1-21. -
További szerzők:Arshad, Sana Alsilibe, Firas Moazzam, Muhammad Farhan Ul Bashir, Bashar Prodhan, Foyez Ahmed Alsalman, Abdullah Vad Attila (1981-) (agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök)
Pályázati támogatás:TKP2021-NKTA-32
Egyéb
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4.

001-es BibID:BIBFORM108818
035-os BibID:(WoS)000956899600001 (Scopus)85151408992
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland : A case study in Syria / Safwan Mohammed, Ali Jouhra, Glory O. Enaruvbe, Bashar Bashir, Mona Barakat, Firas Alsilibe, Luc Cimusa Kulimushi, Abdullah Alsalman, Szilárd Szabó
Dátum:2023
ISSN:1085-3278
Megjegyzések:The development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, Generalized Linear Model (GLM), Random Forest Regression (RF), Elastic Net Regression (EN) and Multiple Adaptive Regression Splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m ??1.50 m ??0.50 m, 0.10 m depth in the soil), combined with 3 different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10?mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18?0.14 kg/m2 per event in KS, 0.14?0.11 kg/m2 per event in SS, and 0.12?0.10 kg/m2 per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r2=0.85; p<0.05, n=24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion (p=0.02) with a medium effect (?2p=0.26). However, the impacts of rainfall events and slope categories on soil erosion were limited (?2p<0.01) and not significant (p>0.05). ML techniques were usually efficient in the prediction, the RF and MARS models were the most accurate: RF had the strongest correlation with the measured values (r=0.85) with a low estimation error (0.06 kg/m2), but MARS's standard deviation (SD) was closer to the recorded values' SD. GLM and and EN were the weakest predictor models. Modelled values of the slightest slope (8%) had the worst accuracies, and the predictions of the 12% slope were the best in all models. This study provides important insights into the usefulness of machine learning techniques and algorithms in predicting the rate of soil erosion and runoff in agricultural dominated landscapes. We highlighted that the RF and MARS algorithms were better predictors of soil erosion and runoff in the coastal region of Syria.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
soil properties
Entisols
GLM
ML-algorithms
Syria
Megjelenés:Land Degradation & Development. - [Epub ahead of print] : - (2023), p.1-38. -
További szerzők:Jouhra, Ali Enaruvbe, Glory O. Bashir, Bashar Barakat, Mona Alsilibe, Firas Cimusa Kulimushi, Luc Alsalman, Abdullah Szabó Szilárd (1974-) (geográfus)
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5.

001-es BibID:BIBFORM101363
035-os BibID:(cikkazonosító)106925 (WoS)000807778300003 (Scopus)85127809340
Első szerző:Mohammed Safwan (agrármérnök)
Cím:A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean / Safwan Mohammed, Ahmed Elbeltagi, Bashar Bashir, Karam Alsafadi, Firas Alsilibe, Abdullah Alsalman, Mojtaba Zeraatpisheh, Adrienn Széles, Endre Harsányi
Dátum:2022
ISSN:0168-1699
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Computers And Electronics In Agriculture. - 197 (2022), p. 1-19. -
További szerzők:Elbeltagi, Ahmed Bashir, Bashar Alsafadi, Karam Alsilibe, Firas Alsalman, Abdullah Zeraatpisheh, Mojtaba Széles Adrienn (1980-) (okleveles agrármérnök) Harsányi Endre (1976-) (agrármérnök)
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