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Article

Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning

by 1,2 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.
Sensors 2019, 19(17), 3717; https://doi.org/10.3390/s19173717
Received: 5 August 2019 / Revised: 22 August 2019 / Accepted: 26 August 2019 / Published: 27 August 2019
This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses. View Full-Text
Keywords: GIS; landslide susceptibility; machine learning; remote sensing; spatial analysis GIS; landslide susceptibility; machine learning; remote sensing; spatial analysis
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MDPI and ACS Style

Lai, J.-S.; Tsai, F. Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors 2019, 19, 3717. https://doi.org/10.3390/s19173717

AMA Style

Lai J-S, Tsai F. Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors. 2019; 19(17):3717. https://doi.org/10.3390/s19173717

Chicago/Turabian Style

Lai, Jhe-Syuan, and Fuan Tsai. 2019. "Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning" Sensors 19, no. 17: 3717. https://doi.org/10.3390/s19173717

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