CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA
As a typical type of natural disaster, landslides may result in injuries to humans, threats to property security, and economic loss. As such, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. Using typically machine learning (ML) to estimate...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-10-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-3-W1-2022/25/2022/isprs-annals-X-3-W1-2022-25-2022.pdf |
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| author | C. Chen L. Fan |
| author_facet | C. Chen L. Fan |
| author_sort | C. Chen |
| collection | DOAJ |
| description | As a typical type of natural disaster, landslides may result in injuries to humans, threats to property security, and economic loss. As such, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. Using typically machine learning (ML) to estimate landslide susceptibility based on a landslide inventory and a set of factors that impact the occurrence of landslides is a common practice. However, in landslide susceptibility assessment, existing DL-based neural network methods use a fully connected layer to optimize the selection of factors, which limits their efficiency in extracting features of those contributing factors. In response to those problems, this study proposed a CNN-LSTM model with an attention mechanism (AM) to avoid the complex optimization of input factors while the same or even better prediction accuracy can be achieved. To compare our method with the existing ones, the historical landslide inventory and the remote sensing data of Kerala, India were used to produce the input variables needed in the methods considered. The results show that our method produced more accurate results, compared to those existing neural network methods (e.g. CNN, LSTM and CNN-LSTM). |
| first_indexed | 2024-04-12T18:00:39Z |
| format | Article |
| id | doaj.art-b00989fecb584e848659933c2375b2e2 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-12T18:00:39Z |
| publishDate | 2022-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-b00989fecb584e848659933c2375b2e22022-12-22T03:22:11ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502022-10-01X-3-W1-2022253010.5194/isprs-annals-X-3-W1-2022-25-2022CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIAC. Chen0L. Fan1Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, ChinaAs a typical type of natural disaster, landslides may result in injuries to humans, threats to property security, and economic loss. As such, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. Using typically machine learning (ML) to estimate landslide susceptibility based on a landslide inventory and a set of factors that impact the occurrence of landslides is a common practice. However, in landslide susceptibility assessment, existing DL-based neural network methods use a fully connected layer to optimize the selection of factors, which limits their efficiency in extracting features of those contributing factors. In response to those problems, this study proposed a CNN-LSTM model with an attention mechanism (AM) to avoid the complex optimization of input factors while the same or even better prediction accuracy can be achieved. To compare our method with the existing ones, the historical landslide inventory and the remote sensing data of Kerala, India were used to produce the input variables needed in the methods considered. The results show that our method produced more accurate results, compared to those existing neural network methods (e.g. CNN, LSTM and CNN-LSTM).https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-3-W1-2022/25/2022/isprs-annals-X-3-W1-2022-25-2022.pdf |
| spellingShingle | C. Chen L. Fan CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA |
| title_full | CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA |
| title_fullStr | CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA |
| title_full_unstemmed | CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA |
| title_short | CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA |
| title_sort | cnn lstm attention deep learning model for mapping landslide susceptibility in kerala india |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-3-W1-2022/25/2022/isprs-annals-X-3-W1-2022-25-2022.pdf |
| work_keys_str_mv | AT cchen cnnlstmattentiondeeplearningmodelformappinglandslidesusceptibilityinkeralaindia AT lfan cnnlstmattentiondeeplearningmodelformappinglandslidesusceptibilityinkeralaindia |