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Article

The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method

by 1,* and 2
1
Department of Civil Engineering, Feng Chia University, Taichung 407, Taiwan
2
True Dreams Construction Company, Taichung 403, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Su-Chin Chen
Water 2021, 13(23), 3348; https://doi.org/10.3390/w13233348
Received: 22 October 2021 / Revised: 19 November 2021 / Accepted: 23 November 2021 / Published: 25 November 2021
With the unique rainfall patterns of typhoons, plum rains, and short-term heavy rainfalls, the frequent landslide and debris flow disasters have caused severe loss to people in Taiwan. In the studies of landslide susceptibility, the information of factors used for analysis was usually annual-based content, and it was assumed that the same elements from different years were independent between each year. However, the occurrence of landslides was usually not simply due to the changes within a year. Instead, landslides were triggered because the factors that affected the potential of landslides reached critical conditions after a cumulative change with time. Therefore, this study had well evaluated the influence of temporal characteristics and the ratios of antecedent landslide areas in the past five years in the landslide potential evaluation model. The analysis was conducted through the random forest (RF) algorithm. Additional rainfall events of 2017 were used to test the proposed model’s performance to understand its practicality. The analysis results show that in the study area, the RF model had considerably acceptable performance. The results have also demonstrated that the antecedent landslide ratios in the past five years were essential to describe the significance of cumulative change with time when conducting potential landslide evaluation. View Full-Text
Keywords: landslide potential; random forest; antecedent landslides; machine learning landslide potential; random forest; antecedent landslides; machine learning
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MDPI and ACS Style

Huang, Y.-M.; Lu, S.-W. The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method. Water 2021, 13, 3348. https://doi.org/10.3390/w13233348

AMA Style

Huang Y-M, Lu S-W. The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method. Water. 2021; 13(23):3348. https://doi.org/10.3390/w13233348

Chicago/Turabian Style

Huang, Yi-Min, and Shao-Wei Lu. 2021. "The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method" Water 13, no. 23: 3348. https://doi.org/10.3390/w13233348

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