Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning
Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python...
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MDPI AG
2024-11-01
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| author | Fudong Ren Koichi Isobe |
| author_facet | Fudong Ren Koichi Isobe |
| author_sort | Fudong Ren |
| collection | DOAJ |
| description | Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e921c35960754d8f9332cb1d9e63cb672024-11-26T17:49:18ZengMDPI AGApplied Sciences2076-34172024-11-0114221057110.3390/app142210571Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep LearningFudong Ren0Koichi Isobe1Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, Hokkaido, JapanFaculty of Engineering, Hokkaido University, Sapporo 060-8628, Hokkaido, JapanAcross the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience.https://www.mdpi.com/2076-3417/14/22/10571landslideCNNwindow sizesampling techniques |
| spellingShingle | Fudong Ren Koichi Isobe Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning Applied Sciences landslide CNN window size sampling techniques |
| title | Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning |
| title_full | Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning |
| title_fullStr | Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning |
| title_full_unstemmed | Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning |
| title_short | Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning |
| title_sort | versatility evaluation of landslide risk with window sizes and sampling techniques based on deep learning |
| topic | landslide CNN window size sampling techniques |
| url | https://www.mdpi.com/2076-3417/14/22/10571 |
| work_keys_str_mv | AT fudongren versatilityevaluationoflandslideriskwithwindowsizesandsamplingtechniquesbasedondeeplearning AT koichiisobe versatilityevaluationoflandslideriskwithwindowsizesandsamplingtechniquesbasedondeeplearning |