Novel hybrid evolutionary algorithms for spatial prediction of floods
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial mode...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2018
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| Online Access: | http://eprints.utm.my/79652/2/BaharinAhmad2018_NovelHybridEvolutionaryAlgorithmsforSpatial.pdf |
| _version_ | 1796863083746951168 |
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| author | Bui, D. T. Panahi, M. Shahabi, H. Singh, V. P. Shirzadi, A. Chapi, K. Khosravi, K. Chen, W. Panahi, S. Li, S. Ahmad, B. B. |
| author_facet | Bui, D. T. Panahi, M. Shahabi, H. Singh, V. P. Shirzadi, A. Chapi, K. Khosravi, K. Chen, W. Panahi, S. Li, S. Ahmad, B. B. |
| author_sort | Bui, D. T. |
| collection | ePrints |
| description | Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas. |
| first_indexed | 2024-03-05T20:21:22Z |
| format | Article |
| id | utm.eprints-79652 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:21:22Z |
| publishDate | 2018 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | utm.eprints-796522019-01-28T04:58:25Z http://eprints.utm.my/79652/ Novel hybrid evolutionary algorithms for spatial prediction of floods Bui, D. T. Panahi, M. Shahabi, H. Singh, V. P. Shirzadi, A. Chapi, K. Khosravi, K. Chen, W. Panahi, S. Li, S. Ahmad, B. B. G70.212-70.215 Geographic information system Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas. Nature Publishing Group 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/79652/2/BaharinAhmad2018_NovelHybridEvolutionaryAlgorithmsforSpatial.pdf Bui, D. T. and Panahi, M. and Shahabi, H. and Singh, V. P. and Shirzadi, A. and Chapi, K. and Khosravi, K. and Chen, W. and Panahi, S. and Li, S. and Ahmad, B. B. (2018) Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports, 8 (1). ISSN 2045-2322 http://dx.doi.org/10.1038/s41598-018-33755-7 DOI:10.1038/s41598-018-33755-7 |
| spellingShingle | G70.212-70.215 Geographic information system Bui, D. T. Panahi, M. Shahabi, H. Singh, V. P. Shirzadi, A. Chapi, K. Khosravi, K. Chen, W. Panahi, S. Li, S. Ahmad, B. B. Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title | Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title_full | Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title_fullStr | Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title_full_unstemmed | Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title_short | Novel hybrid evolutionary algorithms for spatial prediction of floods |
| title_sort | novel hybrid evolutionary algorithms for spatial prediction of floods |
| topic | G70.212-70.215 Geographic information system |
| url | http://eprints.utm.my/79652/2/BaharinAhmad2018_NovelHybridEvolutionaryAlgorithmsforSpatial.pdf |
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