Preprints
https://doi.org/10.5194/nhessd-3-1137-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhessd-3-1137-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
05 Feb 2015
Status: this preprint was under review for the journal NHESS but the revision was not accepted.
Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories
Abstract. This study uses landslide inventory of a single typhoon event and Weight of Evidence (WOE) analysis to establish landslide susceptibility map of the Laonung River in southern Taiwan. Eight factors including lithology, elevation, slope, slope aspect, landform, Normalized Difference Vegetation Index (NDVI), distance to geological structure, and distance to stream are used to evaluate the susceptibility of landslide. Effect analysis and the assessment of grouped factors showed that lithology, slope, elevation, and NDVI are the dominant factors of landslides in the study area. Landslide susceptibility analysis with these four factors achieves over 90% of the AUC (area under curve) of the success rate curve of all eight factors. Four landslide susceptibility models for four typhoons from 2007 to 2009 are established, and each model is cross validated. Results indicate that the best model should be constructed by using landslide inventory close to the landslide occurrence threshold and should reflect the most common spatial rainfall pattern in the study region for ideal simulation and validation results. The prediction accuracy of the best model in this study reached 90.2%. The two highest susceptibility categories (very high and high levels) cover around 80% of the total landslides in the study area.
How to cite. Tseng, C. M., Lin, C. W., and Hsieh, W. D.: Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories, Nat. Hazards Earth Syst. Sci. Discuss., 3, 1137–1173, https://doi.org/10.5194/nhessd-3-1137-2015, 2015.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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- Supplement
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RC C112: 'Review comments', Anonymous Referee #1, 03 Mar 2015
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AC C354: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
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AC C372: 'revised paper', Chih-Ming Tseng, 02 Apr 2015
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AC C374: 'PDF file of reply', Chih-Ming Tseng, 03 Apr 2015
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AC C354: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
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RC C131: 'Landslide susceptibility evaluation', Anonymous Referee #2, 05 Mar 2015
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AC C362: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
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AC C373: 'revised paper', Chih-Ming Tseng, 02 Apr 2015
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AC C375: 'PDF file of reply', Chih-Ming Tseng, 03 Apr 2015
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AC C362: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
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EC C427: 'Editorial Decision', Fausto Guzzetti, 12 Apr 2015
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
-
RC C112: 'Review comments', Anonymous Referee #1, 03 Mar 2015
-
AC C354: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
-
AC C372: 'revised paper', Chih-Ming Tseng, 02 Apr 2015
-
AC C374: 'PDF file of reply', Chih-Ming Tseng, 03 Apr 2015
-
AC C354: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
-
RC C131: 'Landslide susceptibility evaluation', Anonymous Referee #2, 05 Mar 2015
-
AC C362: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
-
AC C373: 'revised paper', Chih-Ming Tseng, 02 Apr 2015
-
AC C375: 'PDF file of reply', Chih-Ming Tseng, 03 Apr 2015
-
AC C362: 'response to reviewer's comments', Chih-Ming Tseng, 02 Apr 2015
-
EC C427: 'Editorial Decision', Fausto Guzzetti, 12 Apr 2015
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Cited
19 citations as recorded by crossref.
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- Prediction of the landslide susceptibility: Which algorithm, which precision? H. Pourghasemi & O. Rahmati 10.1016/j.catena.2017.11.022
- An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India S. Ali et al. 10.1007/s11069-022-05360-5
- Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms W. Chen et al. 10.1007/s10064-017-1004-9
- Landslide susceptibility mapping using state-of-the-art machine learning ensembles B. Pham et al. 10.1080/10106049.2021.1914746
- Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) Tehlike Duyarlılık Analizleri Kapsamında Havza Yönetimi M. UZUN & N. GARİPAĞAOĞLU 10.51800/ecd.1160134
- Use of geographic information system for the vulnerability assessment of landscape in upper Satluj basin of district Kinnaur, Himachal Pradesh, India A. Jamwal et al. 10.1080/24749508.2019.1608410
- Landslides susceptibility assessment by analytical network process: a case study for Kuttiyadi river basin (Western Ghats, southern India) T. Swetha & G. Gopinath 10.1007/s42452-020-03574-5
- Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan A. Asghar et al. 10.1007/s11629-023-8029-2
- Modeling regional landslide susceptibility using dynamic soil moisture profiles R. Ray et al. 10.1007/s11629-018-4896-3
- Landslide Susceptibility Assessment in Constantine Region (NE Algeria) By Means of Statistical Models N. Manchar et al. 10.2478/sgem-2018-0024
- Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda J. Nsengiyumva et al. 10.1111/risa.13359
- Yenişehir (Bursa) Havzası’nın coğrafi karakterizasyonuna dayalı jeoekolojik risk duyarlılığı analizi M. UZUN 10.32003/igge.1326841
- Landslide susceptibility mapping and rock slope stability assessment using frequency ratio and kinematic analysis in the mountains of Mgulat area, Northern Ethiopia G. Berhane et al. 10.1007/s10064-020-01905-9
- Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. 10.5194/nhess-15-1785-2015
- Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. 10.1080/10106049.2022.2120546
- Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model T. Zhang et al. 10.3390/e21020218
- Application of novel framework approach for assessing rainfall induced future landslide hazard to world heritage sites in Indo-Nepal-Bhutan Himalayan region I. Chowdhuri et al. 10.1080/10106049.2022.2134464
- Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment J. Nsengiyumva & R. Valentino 10.1080/19475705.2020.1785555
Saved
Latest update: 25 Apr 2024
C. M. Tseng
CORRESPONDING AUTHOR
Department of Land Management and Development, Chang Jung Christian University, No. 1, Changda Road, Gueiren District, Tainan City, Taiwan
C. W. Lin
Department of Earth Sciences, National Cheng Kung University, No. 1, University Road, Tainan City, Taiwan
W. D. Hsieh
Department of Earth Sciences, National Cheng Kung University, No. 1, University Road, Tainan City, Taiwan
Short summary
This study applies landslide inventory of a single typhoon and WOE analysis to establish landslide susceptibility model. We apply multi-temporal satellite images to map four different scales of event-based new landslide inventories. The most suitable typhoon scale to establish an optimal model is discussed. The prediction accuracy of the landslide susceptibility map in this study shows the advantage of a model established by using an event-based landslide inventory.
This study applies landslide inventory of a single typhoon and WOE analysis to establish...
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