Flood prediction and early warning in south sudan using artificial neural network modelling (ANNM) / (Record no. 179245)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05713namaa22004571i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GICUC |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20260407103304.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260407s2025 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 | 551.5 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 551.5 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | Ph.D |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.16.03.Ph.D.2025.Ab.F |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Abdel Monaim Fakhry Kamel Mohamed, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Flood prediction and early warning in south sudan using artificial neural network modelling (ANNM) / |
| Statement of responsibility, etc. | by Abdel Monaim Fakhry Kamel Mohamed ; Supervisors Prof. Dr. Fawzia Ibrahim Moursy, Prof. Attia Mahmoud El-Tantawi, Prof. Farid Ali Mousa, Prof. Mohamed El-Sayed El-Mahdy. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | التنبؤ بالفيضانات والإنذار المبكر في جنوب السودان باستخدام نمذجة الشبكات العصبية الاصطناعية |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 196 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 (Ph.D)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 187-196. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | The current study is one of the records of Floods on part of the Earth's surface in South Sudan, and used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records from stations along the Blue Nile were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95% accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase in hidden layers. Also, six other algorithms were applied to reach the highest value expected from using one of the artificial intelligence techniques (AI), in predicting floods by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916). |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | الدراسة الحالية هي إحدى سجلات الفيضانات على جزء من سطح الأرض في جنوب السودان، واستخدمت تقنيات الذكاء الاصطناعي كأداة نمذجة لتقدير خطر فيضانات النيل في مدن جنوب السودان. تم استخدام السجلات المناخية من المحطات على طول النيل الأزرق بين عامي 2010 و 2019. لاختبار مدى نجاح النماذج، تم إجراء التنبؤ باستخدام مجموعة متنوعة من المحطات. لتحديد معدل الفيضانات في جنوب السودان بأعلى درجة من الدقة، تم التحقيق في تقنيات الشبكة العصبية الاصطناعية المختلفة. تم إنشاء ستة نماذج للشبكة العصبية الاصطناعية (ANN) ومقارنتها لإظهار التنبؤ بالفيضانات للوصول إلى أقصى مستوى من الدقة وتحسين النتائج (NN، GRNN، RNN، CFNN، PNN، FFNN). أنتجت الشبكة العصبية الاصطناعية (FFNN) أفضل النتائج في الاختبار الأول، حيث وصلت إلى معدل دقة 95٪. تم تقييم ثلاث استراتيجيات أخرى من خلال زيادة عدد الطبقات المخفية للشبكة العصبية إلى عشرة. كما أظهرت الاختبارات التي أجريت على 15 و 25 طبقة مخفية أن الدقة تتغير مع زيادة الطبقات المخفية، كما تم تطبيق ستة خوارزميات أخرى للوصول إلى أعلى قيمة متوقعة من استخدام إحدى تقنيات الذكاء الاصطناعي (AI)، في التنبؤ بالفيضان بطرق التعلم الآلي (ML). تم الوصول إلى أعلى قيمة متوقعة للفيضان من خلال نموذج (Gradient Boosting)، حيث كانت دقة التصنيف (CA) 0.937، تليها (AdaBoost)، (CA 0.916). |
| 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 | Meteorology |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | الأرصاد الجوية |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Flood Prediction |
| -- | Flood Classification |
| -- | Artificial Intelligence |
| -- | Deep Learning |
| -- | South Sudan |
| -- | التنبؤ بالفيضانات |
| -- | تصنيف الفيضانات |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Fawzia Ibrahim Moursy |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Attia Mahmoud El-Tantawi |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Farid Ali Mousa |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mohamed El-Sayed El-Mahdy |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2025 |
| Supervisory body | Fawzia Ibrahim Moursy |
| -- | Attia Mahmoud El-Tantawi |
| -- | Farid Ali Mousa |
| -- | Mohamed El-Sayed El-Mahdy |
| Discussion body | Heshmat Abdel Baset Mohamed Ahmad |
| -- | El Sayed Mohamed Abdel Hamid Robea |
| Universities | Cairo University |
| Faculties | Faculty of African Postgraduate Studies |
| Department | Department of Natural Resources |
| 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 | 07.04.2026 | 93683 | Cai01.16.03.Ph.D.2025.Ab.F | 01010110093683000 | 07.04.2026 | 07.04.2026 | Thesis |