Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS s...
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2025-06-01
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| author | Chuanwei Zhang Dingshuai Liu Paraskevas Tsangaratos Ioanna Ilia Sijin Ma Wei Chen |
| author_facet | Chuanwei Zhang Dingshuai Liu Paraskevas Tsangaratos Ioanna Ilia Sijin Ma Wei Chen |
| author_sort | Chuanwei Zhang |
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| description | The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. |
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| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e46b500b7b614f8b8b5ae1c2f042086b2025-08-20T02:23:00ZengMDPI AGApplied Sciences2076-34172025-06-011511632510.3390/app15116325Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning OptimizationChuanwei Zhang0Dingshuai Liu1Paraskevas Tsangaratos2Ioanna Ilia3Sijin Ma4Wei Chen5Kunming Coal Design and Research Institute Co., Ltd., Kunming 650000, ChinaYunnan Xiaolongtan Mining Bureau Co., Ltd., Kaiyuan 661600, 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, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaThe present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making.https://www.mdpi.com/2076-3417/15/11/6325landslide susceptibility mappingmachine learning optimizationGIS-based analysisRandom ForestsMei CountyChina |
| spellingShingle | Chuanwei Zhang Dingshuai Liu Paraskevas Tsangaratos Ioanna Ilia Sijin Ma Wei Chen Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization Applied Sciences landslide susceptibility mapping machine learning optimization GIS-based analysis Random Forests Mei County China |
| title | Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization |
| title_full | Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization |
| title_fullStr | Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization |
| title_full_unstemmed | Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization |
| title_short | Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization |
| title_sort | enhancing predictive accuracy of landslide susceptibility via machine learning optimization |
| topic | landslide susceptibility mapping machine learning optimization GIS-based analysis Random Forests Mei County China |
| url | https://www.mdpi.com/2076-3417/15/11/6325 |
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