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Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment
Article

Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling

by 1,2, 2,3 and 2,3,*
1
Department of Civil Engineering, Feng Chia University, Taichung 40724, Taiwan
2
Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
3
Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 397; https://doi.org/10.3390/ijgi8090397
Received: 22 July 2019 / Revised: 7 August 2019 / Accepted: 2 September 2019 / Published: 5 September 2019
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
This study explores two modeling issues that may cause uncertainty in landslide susceptibility assessments when different sampling strategies are employed. The first issue is that extracted attributes within a landslide inventory polygon can vary if the sample is obtained from different locations with diverse topographic conditions. The second issue is the mixing problem of landslide inventory that the detection of landslide areas from remotely-sensed data generally includes source and run-out features unless the run-out portion can be removed manually with auxiliary data. To this end, different statistical sampling strategies and the run-out influence on random forests (RF)-based landslide susceptibility modeling are explored for Typhoon Morakot in 2009 in southern Taiwan. To address the construction of models with an extremely high false alarm error or missing error, this study integrated cost-sensitive analysis with RF to adjust the decision boundary to achieve improvements. Experimental results indicate that, compared with a logistic regression model, RF with the hybrid sample strategy generally performs better, achieving over 80% and 0.7 for the overall accuracy and kappa coefficient, respectively, and higher accuracies can be obtained when the run-out is treated as an independent class or combined with a non-landslide class. Cost-sensitive analysis significantly improved the prediction accuracy from 5% to 10%. Therefore, run-out should be separated from the landslide source and labeled as an individual class when preparing a landslide inventory. View Full-Text
Keywords: cost-sensitive analysis; landslide susceptibility; random forests; sampling strategy; Typhoon Morakot cost-sensitive analysis; landslide susceptibility; random forests; sampling strategy; Typhoon Morakot
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MDPI and ACS Style

Lai, J.-S.; Chiang, S.-H.; Tsai, F. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS Int. J. Geo-Inf. 2019, 8, 397. https://doi.org/10.3390/ijgi8090397

AMA Style

Lai J-S, Chiang S-H, Tsai F. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling. ISPRS International Journal of Geo-Information. 2019; 8(9):397. https://doi.org/10.3390/ijgi8090397

Chicago/Turabian Style

Lai, Jhe-Syuan, Shou-Hao Chiang, and Fuan Tsai. 2019. "Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling" ISPRS International Journal of Geo-Information 8, no. 9: 397. https://doi.org/10.3390/ijgi8090397

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