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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...

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Autores principales: Bui, Dieu Tien, Panahi, Mahdi, Shahabi, Himan, Singh, Vijay P., Shirzadi, Ataollah, Chapi, Kamran, Khosravi, Khabat, Chen, Wei, Panahi, Somayeh, Li, Shaojun, Ahmad, Baharin Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193992/
https://www.ncbi.nlm.nih.gov/pubmed/30337603
http://dx.doi.org/10.1038/s41598-018-33755-7
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author Bui, Dieu Tien
Panahi, Mahdi
Shahabi, Himan
Singh, Vijay P.
Shirzadi, Ataollah
Chapi, Kamran
Khosravi, Khabat
Chen, Wei
Panahi, Somayeh
Li, Shaojun
Ahmad, Baharin Bin
author_facet Bui, Dieu Tien
Panahi, Mahdi
Shahabi, Himan
Singh, Vijay P.
Shirzadi, Ataollah
Chapi, Kamran
Khosravi, Khabat
Chen, Wei
Panahi, Somayeh
Li, Shaojun
Ahmad, Baharin Bin
author_sort Bui, Dieu Tien
collection PubMed
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.
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spelling pubmed-61939922018-10-24 Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods Bui, Dieu Tien Panahi, Mahdi Shahabi, Himan Singh, Vijay P. Shirzadi, Ataollah Chapi, Kamran Khosravi, Khabat Chen, Wei Panahi, Somayeh Li, Shaojun Ahmad, Baharin Bin Sci Rep Article 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 UK 2018-10-18 /pmc/articles/PMC6193992/ /pubmed/30337603 http://dx.doi.org/10.1038/s41598-018-33755-7 Text en © The Author(s) 2018, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bui, Dieu Tien
Panahi, Mahdi
Shahabi, Himan
Singh, Vijay P.
Shirzadi, Ataollah
Chapi, Kamran
Khosravi, Khabat
Chen, Wei
Panahi, Somayeh
Li, Shaojun
Ahmad, Baharin Bin
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193992/
https://www.ncbi.nlm.nih.gov/pubmed/30337603
http://dx.doi.org/10.1038/s41598-018-33755-7
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