Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms
Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms f...
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| Veröffentlicht in: | Scientific reports Jg. 15; H. 1; S. 22787 - 31 |
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| Abstract | Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms—black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)—were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA–MLP, FSA–MLP, MVO–MLP, and HBO–MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies. |
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| AbstractList | Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms-black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)-were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA-MLP, FSA-MLP, MVO-MLP, and HBO-MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies. Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms-black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)-were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA-MLP, FSA-MLP, MVO-MLP, and HBO-MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies.Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms-black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)-were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA-MLP, FSA-MLP, MVO-MLP, and HBO-MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies. Abstract Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms—black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)—were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA–MLP, FSA–MLP, MVO–MLP, and HBO–MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies. |
| ArticleNumber | 22787 |
| Author | Dehrashid, Atefeh Ahmadi Moayedi, Hossein Ikram, Rana Muhammad Adnan Wang, Mo |
| Author_xml | – sequence: 1 givenname: Rana Muhammad Adnan surname: Ikram fullname: Ikram, Rana Muhammad Adnan organization: Water Science and Environmental Research Centre, College of Chemistry and Environmental Engineering, Shenzhen University, College of Architecture and Urban Planning, Guangzhou University, Center for Global Health Research, , Saveetha Institute of Medical and Technical Sciences – sequence: 2 givenname: Mo surname: Wang fullname: Wang, Mo organization: College of Architecture and Urban Planning, Guangzhou University – sequence: 3 givenname: Hossein surname: Moayedi fullname: Moayedi, Hossein organization: Institute of Research and Development, Duy Tan University, School of Engineering and Technology, Duy Tan University – sequence: 4 givenname: Atefeh Ahmadi surname: Dehrashid fullname: Dehrashid, Atefeh Ahmadi email: Atefeh.ahmadi@uok.ac.ir organization: Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Department of Zrebar Lake Socio-economic Research, Kurdistan Studies Institute, University of Kurdistan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40594128$$D View this record in MEDLINE/PubMed |
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| Keywords | Spatial statistical analysis Novel and technical processes Sustainable development Optimized neural network Flood susceptibility map |
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| SubjectTerms | 631/158/1144 704/4111 Flood susceptibility map Humanities and Social Sciences multidisciplinary Novel and technical processes Optimized neural network Science Science (multidisciplinary) Spatial statistical analysis Sustainable development |
| Title | Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms |
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