Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers
This study aims to map flood susceptibility in the Qaa’Jahran watersheds located in Dhamar, Yemen, using geoprocessing and computational techniques. Historical flood data and SAR imagery were used to monitor and create a flood inventory map. The artificial neutral network (ANN) was trained using a n...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2243884 |
| _version_ | 1797681564230877184 |
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| author | Ahmed M. Al-Areeq Radhwan A. A. Saleh Abdulnoor A. J. Ghanim Mustafa Ghaleb Nabil M. Al‑Areeq Ebrahim Al-Wajih |
| author_facet | Ahmed M. Al-Areeq Radhwan A. A. Saleh Abdulnoor A. J. Ghanim Mustafa Ghaleb Nabil M. Al‑Areeq Ebrahim Al-Wajih |
| author_sort | Ahmed M. Al-Areeq |
| collection | DOAJ |
| description | This study aims to map flood susceptibility in the Qaa’Jahran watersheds located in Dhamar, Yemen, using geoprocessing and computational techniques. Historical flood data and SAR imagery were used to monitor and create a flood inventory map. The artificial neutral network (ANN) was trained using a novel algorithm called GWO_LM, which is a hybridization between the Levenberg-Marquardt algorithm (LM) and Grey Wolf Optimizer (GWO) meta-heuristic algorithm and compared the results with state of art machine learning algorithms. The GWO_LM_ANN model exhibited excellent performance in the evaluation, achieving a precision of 97.92%, sensitivity of 100%, specificity of 100%, F1 score of 98.95%, accuracy of 98.75% and AUC of 98.48. This indicates that using GWO_LM for training ANN enhanced the searching process for the optimal weights, resulting in outperforming other state-of-the-art models. The findings hold significant implications for disaster preparedness and response in the Qaa’Jahran watersheds, enabling targeted and efficient non-structural solutions to mitigate the detrimental effects of flash floods in particularly sensitive locations. The use of the previously unexplored GWO_LM model represents a notable advancement in flood susceptibility assessment, surpassing traditional methods and offering novel insights to the existing literature. |
| first_indexed | 2024-03-11T23:46:42Z |
| format | Article |
| id | doaj.art-19014fa3900943f39935b49e64c212cd |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:46:42Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-19014fa3900943f39935b49e64c212cd2023-09-19T09:13:18ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2023.22438842243884Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizersAhmed M. Al-Areeq0Radhwan A. A. Saleh1Abdulnoor A. J. Ghanim2Mustafa Ghaleb3Nabil M. Al‑Areeq4Ebrahim Al-Wajih5Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM)Mechatronics Engineering Department, Kocaeli UniversityCivil Engineering Department, College of Engineering, Najran UniversityInterdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals (KFUPM)Department of Geology and Environment, Thamar UniversitySociety Development & Continuing Education Center, Hodeidah University, AlduraihimiThis study aims to map flood susceptibility in the Qaa’Jahran watersheds located in Dhamar, Yemen, using geoprocessing and computational techniques. Historical flood data and SAR imagery were used to monitor and create a flood inventory map. The artificial neutral network (ANN) was trained using a novel algorithm called GWO_LM, which is a hybridization between the Levenberg-Marquardt algorithm (LM) and Grey Wolf Optimizer (GWO) meta-heuristic algorithm and compared the results with state of art machine learning algorithms. The GWO_LM_ANN model exhibited excellent performance in the evaluation, achieving a precision of 97.92%, sensitivity of 100%, specificity of 100%, F1 score of 98.95%, accuracy of 98.75% and AUC of 98.48. This indicates that using GWO_LM for training ANN enhanced the searching process for the optimal weights, resulting in outperforming other state-of-the-art models. The findings hold significant implications for disaster preparedness and response in the Qaa’Jahran watersheds, enabling targeted and efficient non-structural solutions to mitigate the detrimental effects of flash floods in particularly sensitive locations. The use of the previously unexplored GWO_LM model represents a notable advancement in flood susceptibility assessment, surpassing traditional methods and offering novel insights to the existing literature.http://dx.doi.org/10.1080/10106049.2023.2243884flash flooddeep learninggisremote sensingqaa’jahran |
| spellingShingle | Ahmed M. Al-Areeq Radhwan A. A. Saleh Abdulnoor A. J. Ghanim Mustafa Ghaleb Nabil M. Al‑Areeq Ebrahim Al-Wajih Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers Geocarto International flash flood deep learning gis remote sensing qaa’jahran |
| title | Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers |
| title_full | Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers |
| title_fullStr | Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers |
| title_full_unstemmed | Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers |
| title_short | Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers |
| title_sort | flood hazard assessment in yemen using a novel hybrid approach of grey wolf and levenberg marquardt optimizers |
| topic | flash flood deep learning gis remote sensing qaa’jahran |
| url | http://dx.doi.org/10.1080/10106049.2023.2243884 |
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