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...

Full description

Saved in:
Bibliographic Details
Main Authors: 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.
Format: Article
Language:English
Published: Nature Publishing Group 2018
Subjects:
Online Access:http://eprints.utm.my/id/eprint/79652/2/BaharinAhmad2018_NovelHybridEvolutionaryAlgorithmsforSpatial.pdf
http://eprints.utm.my/id/eprint/79652/
http://dx.doi.org/10.1038/s41598-018-33755-7
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
Language: English
id my.utm.79652
record_format eprints
spelling my.utm.796522019-01-28T04:58:25Z http://eprints.utm.my/id/eprint/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/id/eprint/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
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic G70.212-70.215 Geographic information system
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
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.
format Article
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.
title Novel hybrid evolutionary algorithms for spatial prediction of floods
title_short 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_sort novel hybrid evolutionary algorithms for spatial prediction of floods
publisher Nature Publishing Group
publishDate 2018
url http://eprints.utm.my/id/eprint/79652/2/BaharinAhmad2018_NovelHybridEvolutionaryAlgorithmsforSpatial.pdf
http://eprints.utm.my/id/eprint/79652/
http://dx.doi.org/10.1038/s41598-018-33755-7
_version_ 1643658253937147904