Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/23/3854 |
| _version_ | 1797546866315886592 |
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| author | Wei Chen Yunzhi Chen Paraskevas Tsangaratos Ioanna Ilia Xiaojing Wang |
| author_facet | Wei Chen Yunzhi Chen Paraskevas Tsangaratos Ioanna Ilia Xiaojing Wang |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool. |
| first_indexed | 2024-03-10T14:35:18Z |
| format | Article |
| id | doaj.art-bf12cf2ac45e4268bf41b0890f3ea4d5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:35:18Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bf12cf2ac45e4268bf41b0890f3ea4d52023-11-20T22:14:26ZengMDPI AGRemote Sensing2072-42922020-11-011223385410.3390/rs12233854Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility AssessmentsWei Chen0Yunzhi Chen1Paraskevas Tsangaratos2Ioanna Ilia3Xiaojing Wang4College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, GreeceCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaThe main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.https://www.mdpi.com/2072-4292/12/23/3854landslide susceptibilityfeature selectionoptimizing structural parametersevolutionary algorithmsgenetic algorithmsparticle swarm optimization |
| spellingShingle | Wei Chen Yunzhi Chen Paraskevas Tsangaratos Ioanna Ilia Xiaojing Wang Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments Remote Sensing landslide susceptibility feature selection optimizing structural parameters evolutionary algorithms genetic algorithms particle swarm optimization |
| title | Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments |
| title_full | Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments |
| title_fullStr | Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments |
| title_full_unstemmed | Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments |
| title_short | Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments |
| title_sort | combining evolutionary algorithms and machine learning models in landslide susceptibility assessments |
| topic | landslide susceptibility feature selection optimizing structural parameters evolutionary algorithms genetic algorithms particle swarm optimization |
| url | https://www.mdpi.com/2072-4292/12/23/3854 |
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