Predicting green water footprint of sugarcane and cotton crops using hybrid machine learning algorithms based on multi-source data in Sudan / (Record no. 176890)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06362namaa22004451i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20251221143315.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251221s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 631.305 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 631.305 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.07.02.M.Sc.2025.Ro.P |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Rogaia Haroun Al-Tahir Mohamed, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Predicting green water footprint of sugarcane and cotton crops using hybrid machine learning algorithms based on multi-source data in Sudan / |
| Statement of responsibility, etc. | by Rogaia Haroun Al-Tahir Mohamed ; Supervisors Dr. Mohamed El-Sayed Abuarab, Dr. Abd Al Rahman Sayed Ahmed, Dr. Sarah Awad Helalia. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | التنبؤ بالبصمة المائية الخضراء لمحاصيل قصب السكر والقطن باستخدام خوارزميات التعلم الآلي الهجينة القائمة عـلى بيانات متعددة المصادر في الســــودان |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 133 pages : |
| Other physical details | illustrations ; |
| Dimensions | 25 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 117-133. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | Water scarcity and climate change are two major challenges facing Sudan, which have led <br/>to the migration of many people. The main objective of this study is to evaluate the <br/>potential of single and hybrid machine learning (ML) models in predicting the Green <br/>Water Footprint (GWFP) of sugarcane and cotton under the influence of climate change. <br/>The study will analyze the effects of different input factors, including climate, crop and <br/>remote sensing data, to determine the impact of these factors during the period from 2001 <br/>to 2020. Seven models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), <br/>Support Vector Regression (SVR), Hybrid RF-XGB, RF-SVR, XGB-SVR and RF-XGB<br/>SVR were applied with five scenarios. In reed, the highest MBE was obtained under RF <br/>and Sc3 and was 5.14 m3 ton-1 followed by RF-SVR with 5.05 m3 ton-1, while the weakest <br/>MBE was 0.03 under RF-SVR and Sc1. The highest R2 values were achieved using the <br/>SVR model for all scenarios. What is most noticeable is that the R2 values of the dual <br/>hybrid models were higher than those of the triple hybrid models. The highest NSE value <br/>was 0.98 under Sc2 (climatic parameters) and XGB-SVR, while the lowest NSE value <br/>was recorded with SVR and Sc3 (remote sensing parameters) and was 0.09. RMSE did <br/>not have a consistent trend across all ML model combinations and different scenarios but <br/>what is noticeable under all statistical evaluation indices is that Sc3 has the worst <br/>evaluation dealing with remote sensing parameters (EVI, NDVI, SAVI, and NDWI). The <br/>highest significant impact on GWFP came from effective rainfall at 81.67% followed by <br/>relative humidity (RH) at 7.5% and then maximum temperature at 5.24%. The conclusion <br/>from the study is that when predicting GWFP for sugarcane, individual models achieved <br/>equal and in some cases greater results than the dual and triple hybrid models. In the same <br/>context, remote sensing indices had no positive impact on GWFP prediction, with Sc3 <br/>reflecting the lowest values for all statistical parameters of all models used, so the study <br/>recommends SVR with Sc1 or Sc4 depending on data availability. While in cotton, the <br/>maximum and minimum RMSE values ranged between 31.35 m3 ton-1 and 166.37 m3 ton<br/>1<br/>, based on the hybrid model RF-XGB-SVR and RF model, respectively, under Sc5 and <br/>(Peeff, Tmax) achieved the highest R2 values using hybrid ML models, whether double <br/>or triple, across all scenarios, reaching values of 1.0 or 0.99. The lowest R2 value, recorded <br/>at 0.0676, was observed under SVR and Sc3, closely followed by XGB and Sc3 with a <br/>value of 0.0767. The study recommends the use of hybrid models to reduce the error term <br/>in predicting GWFP of sugarcane and cotton. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | ندرة 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 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Agricultural Engineering |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | هندسة زراعية |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | GWFP |
| -- | Sugarcane |
| -- | Cotton |
| -- | Climate parameters |
| -- | Remote sensing Indices |
| -- | Machine learning models |
| -- | Single and hybrid models |
| -- | إنتاجية المياه الجوفية في قصب السكر و القطن |
| -- | معايير المناخ |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mohamed El-Sayed Abuarab |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Abd Al Rahman Sayed Ahmed |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sarah Awad Helalia |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2025 |
| Supervisory body | Mohamed El-Sayed Abuarab |
| -- | Abd Al Rahman Sayed Ahmed |
| -- | Sarah Awad Helalia |
| Universities | Cairo University |
| Faculties | Faculty of Agricultural |
| Department | Department of Agricultural Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 21.12.2025 | 92872 | Cai01.07.02.M.Sc.2025.Ro.P | 01010110092872000 | 21.12.2025 | 21.12.2025 | Thesis |