Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania
Study region: Prahova river basin located in the central-southern region of Romania. Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independ...
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Elsevier B.V.
2025
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| author | Diaconu D.C. Costache R. Towfiqul Islam A.R.M. Pandey M. Pal S.C. Mishra A.P. Pande C.B. |
| author2 | 57189031449 |
| author_facet | 57189031449 Diaconu D.C. Costache R. Towfiqul Islam A.R.M. Pandey M. Pal S.C. Mishra A.P. Pande C.B. |
| author_sort | Diaconu D.C. |
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| content_provider | Universiti Tenaga Nasional |
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| continent | Asia |
| country | Malaysia |
| description | Study region: Prahova river basin located in the central-southern region of Romania. Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the region: The PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin. ? 2024 The Authors |
| format | Article |
| id | my.uniten.dspace-36429 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-364292025-03-03T15:42:23Z Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania Diaconu D.C. Costache R. Towfiqul Islam A.R.M. Pandey M. Pal S.C. Mishra A.P. Pande C.B. 57189031449 55888132500 57919747800 57612960200 57208776491 57219913061 57193547008 Study region: Prahova river basin located in the central-southern region of Romania. Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the region: The PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin. ? 2024 The Authors Final 2025-03-03T07:42:23Z 2025-03-03T07:42:23Z 2024 Article 10.1016/j.ejrh.2024.101892 2-s2.0-85198371094 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198371094&doi=10.1016%2fj.ejrh.2024.101892&partnerID=40&md5=23df0c91b36fad97580ee57ddf8fb4d7 https://irepository.uniten.edu.my/handle/123456789/36429 54 101892 Elsevier B.V. Scopus |
| spellingShingle | Diaconu D.C. Costache R. Towfiqul Islam A.R.M. Pandey M. Pal S.C. Mishra A.P. Pande C.B. Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title | Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title_full | Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title_fullStr | Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title_full_unstemmed | Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title_short | Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania |
| title_sort | developing flood mapping procedure through optimized machine learning techniques. case study: prahova river basin, romania |
| url_provider | http://dspace.uniten.edu.my/ |