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1.

001-es BibID:BIBFORM105966
035-os BibID:(cikkazonosító)6237 (WOS)000904199700001 (Scopus)85144826669
Első szerző:Alsafadi, Karam
Cím:Assessment of Carbon Productivity Trends and Their Resilience to Drought Disturbances in the Middle East Based on Multi-Decadal Space-Based Datasets / Karam Alsafadi, Shuoben Bi, Bashar Bashir, Safwan Mohammed, Saad Sh. Sammen, Abdullah Alsalman, Amit Kumar Srivastava, Ahmed El Kenawy
Dátum:2022
ISSN:2072-4292
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
SEDI
carbon cycle
eddy covariance
FLUXNET
satellite-based GPP
light-use-efficiency model
Megjelenés:Remote Sensing. - 14 : 24 (2022), p. 1-25. -
További szerzők:Bi, Shuoben Bashir, Bashar Mohammed Safwan (1985-) (agrármérnök) Sammen, Saad Sh. Alsalman, Abdullah Srivastava, Amit Kumar El Kenawy, Ahmed M.
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2.

001-es BibID:BIBFORM100101
035-os BibID:(cikkazonosító)307
Első szerző:Alsafadi, Karam
Cím:Land suitability evaluation for citrus cultivation (Citrus ssp.) in the southwestern Egyptian delta: a GIS technique-based geospatial MCE-AHP framework / Karam Alsafadi, Shuoben Bi, Bashar Bashir, Ali Hagras, Basma Alatrach, Endre Harsanyi, Abdullah Alsalman, Safwan Mohammed
Dátum:2022
ISSN:1866-7511 1866-7538
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Arabian Journal of Geosciences. - 15 : 3 (2022), p. 1-17. -
További szerzők:Bi, Shuoben Bashir, Bashar Hagras, Ali Alatrach, Basma Harsányi Endre (1976-) (agrármérnök) Alsalman, Abdullah Mohammed Safwan (1985-) (agrármérnök)
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3.

001-es BibID:BIBFORM117451
035-os BibID:(cikkazonosító)100323 (WoS)001134137300001 (Scopus)85179467649
Első szerző:Altouma, Ahmed
Cím:An environmental impact assessment of Saudi Arabia's vision 2030 for sustainable urban development : a policy perspective on greenhouse gas emissions / Ahmed Altouma, Bashar Bashir, Behnam Ata, Akasairi Ocwa, Abdullah Alsalman, Endre Harsányi, Safwan Mohammed
Dátum:2024
ISSN:2665-9727
Megjegyzések:Globally, countries are legitimizing actions to curtail the malevolent impacts of environmental degradation. This study examined the interaction between CO2 emissions and selected economic variables within the framework of Saudi Arabia's Vision 2030. The Autoregressive distributed lag model (ARDL) was used to analyze the long-run relationships and short-run dynamics between studied variables (1970-2020). The Mann-Kendall (MK) test revealed a significant (p < 0.05) positive increase of GHGs emissions from all sectors across the KSA. The highest increased were captured at the electricity and heat by 7345454.47 tonnes of carbon dioxide-equivalents/year (p < 0.05). On the hand, the ARDL model indicates that GDP, agriculture, industry, services, and oil production have short-term effects on the environment through CO2 emissions. Therefore, GDP, agriculture, services and oil production contribute to increases in CO2 emissions. While industry contributes to decrease in CO2 emissions. The ARDL model also showed that an increase in GDP of 1 percent increases CO2 emissions by 3.46 percent, while an increase in oil production of 1 percent increases CO2 emissions by 4.04 percent. However, an increase in industry of 1 percent decreases CO2 emissions by 7.25 percent. The output of this research has a policy implication for addressing environmental concerns in the country.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Net-zero emissions
Saudi vision
ARDL
Sustainable societies
Climate change
Megjelenés:Environmental and Sustainability Indicators. - 21 (2024), p. 1-13. -
További szerzők:Bashir, Bashar Ata Behnam (1991-) (Geográfus PhD hallgató) Ocwa, Akasairi (1987-) (Crop scientist) Alsalman, Abdullah Harsányi Endre (1976-) (agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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4.

001-es BibID:BIBFORM111452
035-os BibID:(cikkazonosító)1297 (Scopus)85160411163 (WoS)000994795300001
Első szerző:Harsányi Endre (agrármérnök)
Cím:Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe / Endre Harsányi, Bashar Bashir, Sana Arshad, Akasairi Ocwa, Attila Vad, Abdullah Alsalman, István Bácskai, Tamás Rátonyi, Omar Hijazi, Adrienn Széles, Safwan Mohammed
Dátum:2023
ISSN:2073-4395
Megjegyzések:Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature ?C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-SC1 and ANN-MLP-SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-SC1) and 110.7 (ANN-MLP-SC4). In the testing phase, the ANN-MLP-SC4 had the highest r value (0.96), followed by ANN-MLP-SC1 (0.94) and RF-SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-SC4 and ANN-MLP-SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
maize yield
climate
multilayer perceptron
random forest
optimum model
Megjelenés:Agronomy-Basel. - 13 : 5 (2023), p. 1-22. -
További szerzők:Bashir, Bashar Arshad, Sana Ocwa, Akasairi (1987-) (Crop scientist) Vad Attila (1981-) (agrármérnök) Alsalman, Abdullah Bácskai István (1985-) (Okleveles gépészmérnök) Rátonyi Tamás (1967-) (agrármérnök) Hijazi, Omar Széles Adrienn (1980-) (okleveles agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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5.

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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6.

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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7.

001-es BibID:BIBFORM097275
035-os BibID:(cikkazonosító)6495 (WoS)000713999600001 (Scopus)85125205821
Első szerző:Harsányi Endre (agrármérnök)
Cím:GHGs Emission from the Agricultural Sector within EU-28: A Multivariate Analysis Approach / Endre Harsányi, Bashar Bashir, Gafar Almhamad, Omar Hijazi, Mona Maze, Ahmed Elbeltagi, Abdullah Alsalman, Glory O. Enaruvbe, Safwan Mohammed, Szilárd Szabó
Dátum:2021
ISSN:1996-1073
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Energies. - 14 : 20 (2021), p. 1-18. -
További szerzők:Bashir, Bashar Gafar, Almhamad (1993-) Hijazi, Omar Maze, Mona Elbeltagi, Ahmed Alsalman, Abdullah Enaruvbe, Glory O. Mohammed Safwan (1985-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
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8.

001-es BibID:BIBFORM119276
035-os BibID:(Cikkazonosító)130968
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)
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9.

001-es BibID:BIBFORM118218
035-os BibID:(cikkazonosító)108690
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe / Safwan Mohammed, Sana Arshad, Bashar Bashir, Attila Vad, Abdullah Alsalman, Endre Harsányi
Dátum:2024
ISSN:0378-3774
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
rainwater chemistry
sodium adsorption ratio
multilayer perceptron
agriculture water optimization
Hungary
Megjelenés:Agricultural Water Management. - 293 (2024), p. 1-15. -
További szerzők:Arshad, Sana Bashir, Bashar Vad Attila (1981-) (agrármérnök) Alsalman, Abdullah Harsányi Endre (1976-) (agrármérnök)
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10.

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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11.

001-es BibID:BIBFORM102491
035-os BibID:(cikkazonosító)8838 (WOS)000802776400049 (Scopus)85130738624
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Assessing the impacts of agricultural drought (SPI/SPEI) on maize and wheat yields across Hungary / Safwan Mohammed, Karam Alsafadi, Glory O. Enaruvbe, Bashar Bashir, Ahmed Elbeltagi, Adrienn Széles, Abdullah Alsalman, Endre Harsanyi
Dátum:2022
ISSN:2045-2322
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Scientific Reports. - 12 : 1 (2022), p. 1-13. -
További szerzők:Alsafadi, Karam Enaruvbe, Glory O. Bashir, Bashar Elbeltagi, Ahmed Széles Adrienn (1980-) (okleveles agrármérnök) Alsalman, Abdullah Harsányi Endre (1976-) (agrármérnök)
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12.

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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