A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility
In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical l...
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Online Access: | http://eprints.utm.my/id/eprint/88121/1/AhmadSafuanARashid2019_ANovelSwarmIntelligence%E2%80%94HarrisHawksOptimizationforSpatialAssessment.pdf http://eprints.utm.my/id/eprint/88121/ http://dx.doi.org/10.3390/s19163590 |
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my.utm.881212020-12-14T23:07:32Z http://eprints.utm.my/id/eprint/88121/ A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility Bui, Dieu Tien Moayedi, Hossein Kalantar, Bahareh Osouli, Abdolreza Pradhan, Biswajeet Nguyen, Hoang A. Rashid, Ahmad Safuan TA Engineering (General). Civil engineering (General) In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern. NLM (Medline) 2019-08-17 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/88121/1/AhmadSafuanARashid2019_ANovelSwarmIntelligence%E2%80%94HarrisHawksOptimizationforSpatialAssessment.pdf Bui, Dieu Tien and Moayedi, Hossein and Kalantar, Bahareh and Osouli, Abdolreza and Pradhan, Biswajeet and Nguyen, Hoang and A. Rashid, Ahmad Safuan (2019) A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility. Sensors (Basel, Switzerland), 19 (16). pp. 1-22. ISSN 1424-8220 http://dx.doi.org/10.3390/s19163590 |
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TA Engineering (General). Civil engineering (General) Bui, Dieu Tien Moayedi, Hossein Kalantar, Bahareh Osouli, Abdolreza Pradhan, Biswajeet Nguyen, Hoang A. Rashid, Ahmad Safuan A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
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In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern. |
format |
Article |
author |
Bui, Dieu Tien Moayedi, Hossein Kalantar, Bahareh Osouli, Abdolreza Pradhan, Biswajeet Nguyen, Hoang A. Rashid, Ahmad Safuan |
author_facet |
Bui, Dieu Tien Moayedi, Hossein Kalantar, Bahareh Osouli, Abdolreza Pradhan, Biswajeet Nguyen, Hoang A. Rashid, Ahmad Safuan |
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Bui, Dieu Tien |
title |
A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
title_short |
A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
title_full |
A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
title_fullStr |
A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
title_full_unstemmed |
A novel Swarm Intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility |
title_sort |
novel swarm intelligence—harris hawks optimization for spatial assessment of landslide susceptibility |
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NLM (Medline) |
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2019 |
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http://eprints.utm.my/id/eprint/88121/1/AhmadSafuanARashid2019_ANovelSwarmIntelligence%E2%80%94HarrisHawksOptimizationforSpatialAssessment.pdf http://eprints.utm.my/id/eprint/88121/ http://dx.doi.org/10.3390/s19163590 |
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13.18916 |