Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility...

Full description

Bibliographic Details
Main Authors: Mohammad Mehrabi, Biswajeet Pradhan, Hossein Moayedi, Abdullah Alamri
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Sensors
Subjects:
landslide susceptibility
gis
remote sensing
anfis
metaheuristic optimization
Online Access:https://www.mdpi.com/1424-8220/20/6/1723
id doaj-8a91304a9fef488f9eacd9b4031a3a3a
record_format Article
spelling doaj-8a91304a9fef488f9eacd9b4031a3a3a2020-11-25T03:10:06ZengMDPI AGSensors1424-82202020-03-01206172310.3390/s20061723s20061723Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic TechniquesMohammad Mehrabi0Biswajeet Pradhan1Hossein Moayedi2Abdullah Alamri3Department of Civil Engineering, Kermanshah University of Technology, 6715685420 Kermanshah, IranThe Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, AustraliaInformetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Geology & Geophysics, College of Science, King Saud Univ., P.O. Box 2455, Riyadh 11451, Saudi ArabiaFour state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.https://www.mdpi.com/1424-8220/20/6/1723landslide susceptibilitygisremote sensinganfismetaheuristic optimization
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Mehrabi
Biswajeet Pradhan
Hossein Moayedi
Abdullah Alamri
spellingShingle Mohammad Mehrabi
Biswajeet Pradhan
Hossein Moayedi
Abdullah Alamri
Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
Sensors
landslide susceptibility
gis
remote sensing
anfis
metaheuristic optimization
author_facet Mohammad Mehrabi
Biswajeet Pradhan
Hossein Moayedi
Abdullah Alamri
author_sort Mohammad Mehrabi
title Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
title_short Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
title_full Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
title_fullStr Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
title_full_unstemmed Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques
title_sort optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-03-01
description Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.
topic landslide susceptibility
gis
remote sensing
anfis
metaheuristic optimization
url https://www.mdpi.com/1424-8220/20/6/1723
work_keys_str_mv AT mohammadmehrabi optimizinganadaptiveneurofuzzyinferencesystemforspatialpredictionoflandslidesusceptibilityusingfourstateoftheartmetaheuristictechniques
AT biswajeetpradhan optimizinganadaptiveneurofuzzyinferencesystemforspatialpredictionoflandslidesusceptibilityusingfourstateoftheartmetaheuristictechniques
AT hosseinmoayedi optimizinganadaptiveneurofuzzyinferencesystemforspatialpredictionoflandslidesusceptibilityusingfourstateoftheartmetaheuristictechniques
AT abdullahalamri optimizinganadaptiveneurofuzzyinferencesystemforspatialpredictionoflandslidesusceptibilityusingfourstateoftheartmetaheuristictechniques
_version_ 1724660605549281280

Similar Items