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001-es BibID:	BIBFORM132060
035-os BibID:	(Scopus)105012213407 (WoS)001545275400007
Első szerző:	Mohammed Safwan (agrármérnök)
Cím:	Advanced attention-driven deep learning architectures for multi-depth soil temperature prediction / Safwan Mohammed, Sana Arshad, Akasairi Ocwa, Main Al-Dalahmeh, Ashraf ALDabbas, Muhammad Manhal Alzoubi, Attila Vad, Endre Harsányi
Dátum:	2025
ISSN:	2590-1230
Megjegyzések:	Soil temperature plays a crucial role in ecological stability by supporting balanced processes such as nutrient cycling, and the flow of water and energy. This research aimed to analyze and predict the dynamic relationship of multi depth soil temperature (SDT) at (5 cm, 10 cm, 20 cm, and 50 cm) with meteorological variables using Biwavelet coherence and deep learning models. Four well-structured, attention-based deep learning (DL) architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Transformer were employed for predicting multi depth soil temperature. The wavelet analysis revealed a high coherence of 0.8 to 1.0 between maximum and minimum temperature (Tmax & Tmin) and multi depth soil temperature from 2003 to 2007. Among deep learning models, proposed LSTM with attention layer outperformed GRU, CNN, and Transformer with highest prediction accuracy of R² = 0.951, RMSE = 1.809, and MSE = 3.273 during testing stage. Similarly, it gained highest accuracy of R² = 0.947, RMSE = 1.925, and MSE = 3.706 during validation at a soil depth of 10 cm. Notably, perturbation-based sensitivity analysis also confirmed LSTM as the superior model, with RMSE ranging from 0.180 at the lowest noise level (0.01) to 2.34 at the highest noise level (0.3). The SHAP kernel explanation of best performed LSTM architecture showed the highest positive contribution of Tmax and Tmin in predicting multi depth soil temperature. The output will support sustainability plans in Syria.
Tárgyszavak:	Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Results in Engineering. - 27 (2025), p. 1-17. -
További szerzők:	Arshad, Sana Ocwa, Akasairi (1987-) (Crop scientist) Al-Dalahmeh, Main (1985-) Aldabbas, Ashraf Khaled Abd Elkareem (1983-) (informatikus) Alzoubi, Muhammad Manhal Vad Attila (1981-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök)
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001-es BibID:	BIBFORM130297
035-os BibID:	(Scopus)105007295332
Első szerző:	Mohammed Safwan (agrármérnök)
Cím:	European Union agro-climate policies toward sustainability : analyzing emission trends and land use dynamics (1990-2021) / Safwan Mohammed, Asif Raihan, Sana Arshad, Behnam Ata, Akasairi Ocwa, Main Al-Dalahmeh, Endre Harsanyi
Dátum:	2025
ISSN:	2666-9161
Tárgyszavak:	Természettudományok Környezettudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk
Megjelenés:	Resources, Environment and Sustainability. - 21 (2025), p. 1-21. -
További szerzők:	Raihan, Asif Arshad, Sana Ata, Behnam (1991-) (Geográfus PhD hallgató) Ocwa, Akasairi (1987-) (Crop scientist) Al-Dalahmeh, Main (1985-) Harsányi Endre (1976-) (agrármérnök)
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001-es BibID:	BIBFORM114827
035-os BibID:	(WoS)001105611200001 (Scopus)85174233743
Első szerző:	Mohammed Safwan (agrármérnök)
Cím:	Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990-2019) / Safwan Mohammed, Abid Rashid Gill, Kaushik Ghosal, Main Al-Dalahmeh, Karam Alsafadi, Szabó Szilárd, Judit Oláh, Ali Alkerdi, Akasairi Ocwa, Endre Harsanyi
Dátum:	2024
ISSN:	2666-4984
Megjegyzések:	Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO2 emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO2 emissions occurred in Germany (-7.52 Mt CO2 annually), and the lowest in Latvia (-0.087 Mt CO2 annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO2 emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP(2) leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Climate change Environmental management Sustainable communities Green growth
Megjelenés:	Environmental Science and Ecotechnology. - 18 (2024), p. 1-13. -
További szerzők:	Gill, Abid Rashid Ghosal, Kaushik Al-Dalahmeh, Main (1985-) Alsafadi, Karam Szabó Szilárd (1974-) (geográfus) Oláh Judit (1973-) (agrárközgazdász, logisztika) Alkerdi, Ali Ocwa, Akasairi (1987-) (Crop scientist) Harsányi Endre (1976-) (agrármérnök)
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb
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