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040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a631.305
092			_a631.305 _221
097			_aM.Sc
099			_aCai01.07.02.M.Sc.2025.Ro.P
100	0		_aRogaia Haroun Al-Tahir Mohamed, _epreparation.
245	1	0	_aPredicting green water footprint of sugarcane and cotton crops using hybrid machine learning algorithms based on multi-source data in Sudan / _cby Rogaia Haroun Al-Tahir Mohamed ; Supervisors Dr. Mohamed El-Sayed Abuarab, Dr. Abd Al Rahman Sayed Ahmed, Dr. Sarah Awad Helalia.
246	1	5	_aالتنبؤ بالبصمة المائية الخضراء لمحاصيل قصب السكر والقطن باستخدام خوارزميات التعلم الآلي الهجينة القائمة عـلى بيانات متعددة المصادر في الســــودان
264		0	_c2025.
300			_a133 pages : _billustrations ; _c25 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc)-Cairo University, 2025.
504			_aBibliography: pages 117-133.
520		3	_aWater scarcity and climate change are two major challenges facing Sudan, which have led to the migration of many people. The main objective of this study is to evaluate the potential of single and hybrid machine learning (ML) models in predicting the Green Water Footprint (GWFP) of sugarcane and cotton under the influence of climate change. The study will analyze the effects of different input factors, including climate, crop and remote sensing data, to determine the impact of these factors during the period from 2001 to 2020. Seven models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Hybrid RF-XGB, RF-SVR, XGB-SVR and RF-XGB SVR were applied with five scenarios. In reed, the highest MBE was obtained under RF and Sc3 and was 5.14 m3 ton-1 followed by RF-SVR with 5.05 m3 ton-1, while the weakest MBE was 0.03 under RF-SVR and Sc1. The highest R2 values were achieved using the SVR model for all scenarios. What is most noticeable is that the R2 values of the dual hybrid models were higher than those of the triple hybrid models. The highest NSE value was 0.98 under Sc2 (climatic parameters) and XGB-SVR, while the lowest NSE value was recorded with SVR and Sc3 (remote sensing parameters) and was 0.09. RMSE did not have a consistent trend across all ML model combinations and different scenarios but what is noticeable under all statistical evaluation indices is that Sc3 has the worst evaluation dealing with remote sensing parameters (EVI, NDVI, SAVI, and NDWI). The highest significant impact on GWFP came from effective rainfall at 81.67% followed by relative humidity (RH) at 7.5% and then maximum temperature at 5.24%. The conclusion from the study is that when predicting GWFP for sugarcane, individual models achieved equal and in some cases greater results than the dual and triple hybrid models. In the same context, remote sensing indices had no positive impact on GWFP prediction, with Sc3 reflecting the lowest values for all statistical parameters of all models used, so the study recommends SVR with Sc1 or Sc4 depending on data availability. While in cotton, the maximum and minimum RMSE values ranged between 31.35 m3 ton-1 and 166.37 m3 ton 1 , based on the hybrid model RF-XGB-SVR and RF model, respectively, under Sc5 and (Peeff, Tmax) achieved the highest R2 values using hybrid ML models, whether double or triple, across all scenarios, reaching values of 1.0 or 0.99. The lowest R2 value, recorded at 0.0676, was observed under SVR and Sc3, closely followed by XGB and Sc3 with a value of 0.0767. The study recommends the use of hybrid models to reduce the error term in predicting GWFP of sugarcane and cotton.
520		3	_aندرة المياه وتغير المناخ هما تحديان رئيسيان يواجههما السودان، مما أدى إلى هجرة الكثير من الناس. الهدف الرئيسي من هذه الدراسة هو تقييم إمكانات نماذج التعلم الآلي سواء كانت فردية أو هجينة في التنبؤ ببصمة المياه الخضراء (GWFP) لقصب السكر والقطن تحت تأثير تغير المناخ. ستقوم الدراسة بتحليل تأثيرات عوامل المدخلات المختلفة، بما في ذلك البيانات المناخية والمحاصيل والاستشعار عن بعد، لتحديد تأثير هذه العوامل خلال الفترة من 2001 إلى 2020. تم تطبيق سبعة نماذج، الغابة العشوائية (RF)، التدرج الشديد التعزيز (XGBoost)، ودعم الانحدار المتجه (SVR)، وHybrid RF-XGB، وRF-SVR، وXGB-SVR، وRF-XGB-SVR مع خمسة سيناريوهات. في نبات القصب تم الحصول على أعلى MBE تحت RF وSc3 وكان 5.14 م3 طن-1 يليه RF-SVR بمقدار 5.05 م3 طن-1، بينما كان أضعف MBE 0.03 تحت RF-SVR و Sc1 تم تحقيق أعلى قيم R2 باستخدام نموذج SVR لجميع السيناريوهات
530			_aIssues also as CD.
546			_aText in English and abstract in Arabic & English.
650		0	_aAgricultural Engineering
650		0	_aهندسة زراعية
653		1	_aGWFP _aSugarcane _aCotton _aClimate parameters _aRemote sensing Indices _aMachine learning models _aSingle and hybrid models _aإنتاجية المياه الجوفية في قصب السكر و القطن _aمعايير المناخ
700	0		_aMohamed El-Sayed Abuarab _ethesis advisor.
700	0		_aAbd Al Rahman Sayed Ahmed _ethesis advisor.
700	0		_aSarah Awad Helalia _ethesis advisor.
900			_b01-01-2025 _cMohamed El-Sayed Abuarab _cAbd Al Rahman Sayed Ahmed _cSarah Awad Helalia _UCairo University _FFaculty of Agricultural _DDepartment of Agricultural Engineering
905			_aShimaa
942			_2ddc _cTH _e21 _n0
999			_c176890