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Open AccessArticle
Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model
by
Wenchao Huangfu
Wenchao Huangfu 1,2,
Haijun Qiu
Haijun Qiu
Haijun Qiu has been a Professor at the College of Urban and Environmental Sciences, Northwest since [...]
Haijun Qiu has been a Professor at the College of Urban and Environmental Sciences, Northwest University since 2019. He also serves as a member of the Landslide and Mudflow Prevention and Control Specialized Committee of the Chinese Society of Soil and Water Conservation and a member of the Digital Mountain Specialized Committee of the Chinese National Committee of the International Society for Digital Earth. He earned his Bachelor’s degree in Environmental Engineering and Ph.D. in Physical Geography from Northwest University in 2007 and 2012, respectively. Afterward, he got a position as a Lecturer at the same university (2012–2015) and was promoted to Associate Professor in 2015. He completed his Postdoctoral Researcher at the Institute of Mountain Hazards and Environment, CAS from 2014 to 2017. He also was a Visiting Scholar at the University of Montana (December 2017–April 2018) and at the University of Michigan (April–December 2018). He is engaged in the research of mountain hazards and resource environment, and won the 13th “Youth Science and Technology Award” of the Chinese Society of Soil and Water Conservation.
1,2,3,*
,
Weicheng Wu
Weicheng Wu 4
,
Yaozu Qin
Yaozu Qin 4
,
Xiaoting Zhou
Xiaoting Zhou 5,
Yang Zhang
Yang Zhang 6,
Mohib Ullah
Mohib Ullah 1 and
Yanfen He
Yanfen He 1
1
College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
2
Institute of Earth Surface System and Hazards, Northwest University, Xi’an 710127, China
3
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
4
Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
5
School of Architectural Engineering, Jiangxi Science and Technology Normal University Nanchang, Nanchang 330013, China
6
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(7), 1039; https://doi.org/10.3390/land13071039 (registering DOI)
Submission received: 25 May 2024
/
Revised: 2 July 2024
/
Accepted: 9 July 2024
/
Published: 10 July 2024
Abstract
A rational landslide susceptibility mapping (LSM) can minimize the losses caused by landslides and enhance the efficiency of disaster prevention and reduction. At present, frequency ratio (FR), information value (IV), and certainty factor (CF) are widely used to quantify the relationships between landslides and their causative factors; however, it remains unclear which method is the most effective. Moreover, existing landslide susceptibility zoning methods lack full automation; thus, the results are full of uncertainties. To address this, the FR, IV, and CF were used to analyze the relationship between landslides and causative factors. Subsequently, three distinct sets of models were developed, namely random forest models (RF_FR, RF_IV, and RF_CF), support vector machine models (SVM_FR, SVM_IV, and SVM_CF), and logistic regression models (LR_FR, LR_IV, and LR_CF) using the analysis results as inputs. A Gaussian mixture model (GMM) was introduced as a new method for landslide susceptibility zoning, classifying the LSM into five distinct levels. An accuracy evaluation of the models and a rationality analysis of the LSM indicated that the FR is superior to the IV and CF in quantifying the relationship between landslides and causative factors. Additionally, the quantile method was employed as a comparative approach to the GMM, further validating the effectiveness of the GMM. This research contributes to more effective and efficient LSM, ultimately enhancing landslide prevention measures.
Share and Cite
MDPI and ACS Style
Huangfu, W.; Qiu, H.; Wu, W.; Qin, Y.; Zhou, X.; Zhang, Y.; Ullah, M.; He, Y.
Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model. Land 2024, 13, 1039.
https://doi.org/10.3390/land13071039
AMA Style
Huangfu W, Qiu H, Wu W, Qin Y, Zhou X, Zhang Y, Ullah M, He Y.
Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model. Land. 2024; 13(7):1039.
https://doi.org/10.3390/land13071039
Chicago/Turabian Style
Huangfu, Wenchao, Haijun Qiu, Weicheng Wu, Yaozu Qin, Xiaoting Zhou, Yang Zhang, Mohib Ullah, and Yanfen He.
2024. "Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model" Land 13, no. 7: 1039.
https://doi.org/10.3390/land13071039
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