Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide
Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely...
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| creator | Moayedi, Hossein Mehrabi, Mohammad Kalantar, Bahareh Abdullahi Mu'azu, Mohammed A. Rashid, Ahmad Safuan Foong, Loke Kok Nguyen, Hoang |
| description | Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model. |
| doi_str_mv | 10.1080/19475705.2019.1650126 |
| format | Article |
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Rashid, Ahmad Safuan ; Foong, Loke Kok ; Nguyen, Hoang</creator><creatorcontrib>Moayedi, Hossein ; Mehrabi, Mohammad ; Kalantar, Bahareh ; Abdullahi Mu'azu, Mohammed ; A. Rashid, Ahmad Safuan ; Foong, Loke Kok ; Nguyen, Hoang</creatorcontrib><description>Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.</description><identifier>ISSN: 1947-5705</identifier><identifier>EISSN: 1947-5713</identifier><identifier>DOI: 10.1080/19475705.2019.1650126</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>Acceleration ; Accuracy ; Adaptive systems ; Algorithms ; ANFIS ; Ant colony optimization ; Artificial neural networks ; Distance ; Earthquake-triggered landslide ; Earthquakes ; Elevation ; Evaluation ; Evolutionary algorithms ; Evolutionary computation ; evolutionary optimization ; Fuzzy logic ; Fuzzy systems ; Gene mapping ; Genetic algorithms ; geographic information system ; Geographic information systems ; Geographical information systems ; Heuristic methods ; Hybrids ; Inference ; Information systems ; landslide susceptibility mapping ; Landslides ; Landslides & mudslides ; Lithology ; Mean square errors ; Model accuracy ; Normalized difference vegetative index ; Particle swarm optimization ; Ranking ; Remote sensing ; Rivers ; Root-mean-square errors ; Seismic activity ; Seismic response ; Slopes ; Vegetation index ; Wetness index</subject><ispartof>Geomatics, natural hazards and risk, 2019-01, Vol.10 (1), p.1879-1911</ispartof><rights>2019 The Author(s). Published by Informa UK Limited Trading as Taylor & Francis Group 2019</rights><rights>2019 The Author(s). Published by Informa UK Limited Trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-dab43ca233d1f435cf2de698b2c8d29412e23d708944272c5b3d00c3346388ef3</citedby><cites>FETCH-LOGICAL-c451t-dab43ca233d1f435cf2de698b2c8d29412e23d708944272c5b3d00c3346388ef3</cites><orcidid>0000-0002-3843-0503 ; 0000-0002-2822-3463 ; 0000-0002-5625-1437</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/19475705.2019.1650126$$EPDF$$P50$$Ginformaworld$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/19475705.2019.1650126$$EHTML$$P50$$Ginformaworld$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27479,27901,27902,59116,59117</link.rule.ids></links><search><creatorcontrib>Moayedi, Hossein</creatorcontrib><creatorcontrib>Mehrabi, Mohammad</creatorcontrib><creatorcontrib>Kalantar, Bahareh</creatorcontrib><creatorcontrib>Abdullahi Mu'azu, Mohammed</creatorcontrib><creatorcontrib>A. Rashid, Ahmad Safuan</creatorcontrib><creatorcontrib>Foong, Loke Kok</creatorcontrib><creatorcontrib>Nguyen, Hoang</creatorcontrib><title>Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide</title><title>Geomatics, natural hazards and risk</title><description>Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.</description><subject>Acceleration</subject><subject>Accuracy</subject><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>ANFIS</subject><subject>Ant colony optimization</subject><subject>Artificial neural networks</subject><subject>Distance</subject><subject>Earthquake-triggered landslide</subject><subject>Earthquakes</subject><subject>Elevation</subject><subject>Evaluation</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>evolutionary optimization</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Gene mapping</subject><subject>Genetic algorithms</subject><subject>geographic information system</subject><subject>Geographic information systems</subject><subject>Geographical information systems</subject><subject>Heuristic methods</subject><subject>Hybrids</subject><subject>Inference</subject><subject>Information systems</subject><subject>landslide susceptibility mapping</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Lithology</subject><subject>Mean square errors</subject><subject>Model accuracy</subject><subject>Normalized difference vegetative index</subject><subject>Particle swarm optimization</subject><subject>Ranking</subject><subject>Remote sensing</subject><subject>Rivers</subject><subject>Root-mean-square errors</subject><subject>Seismic activity</subject><subject>Seismic response</subject><subject>Slopes</subject><subject>Vegetation index</subject><subject>Wetness index</subject><issn>1947-5705</issn><issn>1947-5713</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNp9Uc1u1DAQjhBIVKWPgGSJCxyy9V8S742qorBSVQ7A2XLsSdcrJ148zlbpC_GaeLulR3yxNfP9zPirqveMrhhV9JKtZdd0tFlxytYr1jaU8fZVdXas103HxOuXN23eVheIO1qO4Kqj8qz6cxcPEMh26ZN3SOJAjDP77A9AJphTrIf58XEhfhogwWSB4IIZRvLx6u5m8-MTefB5SxAOkEwgI2SzLSyP2Vtiwn1MpT0iGWIiuDfZFxDOaKE49D74vBCDCIgjTPlojuBx9Lb2k5stOBLM5DB4B--qN4MJCBfP93n16-bLz-tv9e33r5vrq9vayobl2pleCmu4EI4NUjR24A7ateq5VY6vJePAheuoWkvJO26bXjhKrRCyFUrBIM6rzUnXRbPT--RHkxYdjddPhZjutUlluwBaMKc471wvuZW9lMo0ijOhOPTcGQVF68NJa5_i7xkw612c01TG17z8v2g7JbuCak4omyJiguHFlVF9jFj_i1gfI9bPERfe5xOvZBPTaB5iCk5ns4SYhmQm67GM-F-Jv4gVsFA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Moayedi, Hossein</creator><creator>Mehrabi, Mohammad</creator><creator>Kalantar, Bahareh</creator><creator>Abdullahi Mu'azu, Mohammed</creator><creator>A. 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Rashid, Ahmad Safuan</au><au>Foong, Loke Kok</au><au>Nguyen, Hoang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide</atitle><jtitle>Geomatics, natural hazards and risk</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>10</volume><issue>1</issue><spage>1879</spage><epage>1911</epage><pages>1879-1911</pages><issn>1947-5705</issn><eissn>1947-5713</eissn><abstract>Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/19475705.2019.1650126</doi><tpages>33</tpages><orcidid>https://orcid.org/0000-0002-3843-0503</orcidid><orcidid>https://orcid.org/0000-0002-2822-3463</orcidid><orcidid>https://orcid.org/0000-0002-5625-1437</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acceleration Accuracy Adaptive systems Algorithms ANFIS Ant colony optimization Artificial neural networks Distance Earthquake-triggered landslide Earthquakes Elevation Evaluation Evolutionary algorithms Evolutionary computation evolutionary optimization Fuzzy logic Fuzzy systems Gene mapping Genetic algorithms geographic information system Geographic information systems Geographical information systems Heuristic methods Hybrids Inference Information systems landslide susceptibility mapping Landslides Landslides & mudslides Lithology Mean square errors Model accuracy Normalized difference vegetative index Particle swarm optimization Ranking Remote sensing Rivers Root-mean-square errors Seismic activity Seismic response Slopes Vegetation index Wetness index |
| title | Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide |
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