Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning
Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buz?u river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and...
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my.uniten.dspace-366202025-03-03T15:43:27Z Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning Costache R. Pal S.C. Pande C.B. Islam A.R.M.T. Alshehri F. Abdo H.G. 55888132500 57208776491 57193547008 57218543677 57224683617 57193090158 Romania Deep learning Disasters Entropy Land use Learning algorithms Multilayer neural networks Optimization Rivers Watersheds Buz?u river basin Flood potential Learning neural networks Machine-learning Multilayers perceptrons Optimisations Optimization algorithms River basins Romania Stackings algorithm artificial intelligence flooding machine learning mapping modeling optimization stacking Floods Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buz?u river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance � 20%), distance from river (importance � 17.5%), land use (importance � 12%) and TPI (importance � 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35?40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924). ? The Author(s) 2024. Final 2025-03-03T07:43:27Z 2025-03-03T07:43:27Z 2024 Article 10.1007/s13201-024-02131-4 2-s2.0-85187780321 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187780321&doi=10.1007%2fs13201-024-02131-4&partnerID=40&md5=816ac9634095b754debe10731ee0d8a7 https://irepository.uniten.edu.my/handle/123456789/36620 14 4 78 All Open Access; Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus |
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Romania Deep learning Disasters Entropy Land use Learning algorithms Multilayer neural networks Optimization Rivers Watersheds Buz?u river basin Flood potential Learning neural networks Machine-learning Multilayers perceptrons Optimisations Optimization algorithms River basins Romania Stackings algorithm artificial intelligence flooding machine learning mapping modeling optimization stacking Floods |
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Romania Deep learning Disasters Entropy Land use Learning algorithms Multilayer neural networks Optimization Rivers Watersheds Buz?u river basin Flood potential Learning neural networks Machine-learning Multilayers perceptrons Optimisations Optimization algorithms River basins Romania Stackings algorithm artificial intelligence flooding machine learning mapping modeling optimization stacking Floods Costache R. Pal S.C. Pande C.B. Islam A.R.M.T. Alshehri F. Abdo H.G. Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| description |
Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buz?u river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance � 20%), distance from river (importance � 17.5%), land use (importance � 12%) and TPI (importance � 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35?40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924). ? The Author(s) 2024. |
| author2 |
55888132500 |
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55888132500 Costache R. Pal S.C. Pande C.B. Islam A.R.M.T. Alshehri F. Abdo H.G. |
| format |
Article |
| author |
Costache R. Pal S.C. Pande C.B. Islam A.R.M.T. Alshehri F. Abdo H.G. |
| author_sort |
Costache R. |
| title |
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| title_short |
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| title_full |
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| title_fullStr |
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| title_full_unstemmed |
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning |
| title_sort |
flood mapping based on novel ensemble modeling involving the deep learning, harris hawk optimization algorithm and stacking based machine learning |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2025 |
| _version_ |
1825816189796876288 |
| score |
13.244109 |