Comparison of Multi-Criteria Decision Making, Statistics, and Machine Learning Models for Landslide Susceptibility Mapping in Van Yen District, Yen Bai Province, Vietnam - research article

Aug 5, 2026

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Landslides are natural hazards that threaten human lives and infrastructure, making landslide susceptibility mapping essential for identifying at-risk areas. This study compares three representative approaches—Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Random Forest (RF)—to build a susceptibility model for Van Yen District, Yen Bai Province, Vietnam. A 70% dataset trains and 30% tests the models using 13 conditioning factors. Results indicate AHP (AUC=0.842) and FR (AUC=0.852) perform well, while RF achieves the highest accuracy (AUC=0.949), demonstrating strong potential for machine learning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Landslide susceptibility mapping and influencing factors

Method groups: MCDM, statistical, and machine learning

Study objective and comparison design

33 Comparison of Multi-Criteria Decision Making, Statistics, and Machine Learning Models for Landslide Susceptibility Mapping in Van Yen District, Yen Bai Province, Vietnam Khuc, T. D.,1 Truong, X. Q.,2* Tran, V. A.,3 Bui, D. Q.,1 Bui, D. P.,2 Ha, H.,1 Tran, T. H. M.,2 Pham, T. T. T.,2 and Yordanov, V. 4 1Hanoi University of Civil Engineering, Hanoi 100000, Vietnam 2Hanoi University of Natural Resources and Environment, Hanoi 100000, Vietnam E-mail: [txquang@hunre.edu.vn](txquang@hunre.edu.vn) * 3Hanoi University of Mining and Geology, Hanoi 100000, Vietnam 4Department of Civil and Environmental Engineering, Politecnico di Milano, 32, 20133 Milano MI, Italy *Corresponding Author DOI: [https://doi.org/10.52939/ijg.v19i7.2743](https://doi.org/10.52939/ijg.v19i7.2743) Abstract Landslides are natural hazards that pose a significant threat to human lives and infrastructure. Landslide susceptibility mapping aims to classify areas at risk of landslides. Multi-Criteria Decision Making (MCDM) algorithms have the advantage of incorporating expert opinions, while Statistics and Machine Learning models demonstrate greater objectivity. This study compares three representative models, namely Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Random Forest (RF), for developing a landslide susceptibility model in Van Yen District, Yen Bai Province. The classification points for landslides were divided into a 70% training set anda 30% testing set. Thirteen conditioning factors were used to evaluate the landslide's influences. The results show that theAHP and FR models perform well with AUC = 0.842 andAUC = 0.852, respectively, while the RF model outperforms them with AUC = 0.949. The study demonstrates the applicability of these models for analyzing landslide susceptibility in the research area, highlighting the strong potential of machine learning models. Keywords: Frequency Ratio, Landslide, Machine Learning, Multi-Criteria Decision Making, Random Forest 1. Introduction Landslides are a type of natural hazard that occurs when a mass of soil or rock moves from its initial position downward in the form of layers or blocks [1] [2] [3] [4] and [5] . It may cause strong impacts on infrastructure, land use, and result in loss of human life [2] . The causes of landslides stem from various sources, including slope instability due to differences in elevation, slope, environmental and weather conditions, as well as human activities and natural events [6] . Every year, Vietnam records approximately 50 landslide incidents that result in damages to properties and loss of human lives. The frequency and severity of landslides are increasing in the Northwestern mountainous region of Vietnam, causing significant damages. Therefore, early prediction of this natural hazard is highly important [7] . Landslide Susceptibility Model (LSM) is a method used to identify areas at risk of landslides by analyzing the spatial distribution of influencing factors [8] . These factors include terrain characteristics, such as elevation, slope, and aspect, as well as meteorological factors such as annual rainfall and wind [9] . Factors related to river networks, such as flow accumulation and river buffer, as well as geological factors including lithological maps and distances to faults or land cover, are also considered [5] . Data collected through image interpretation or field surveys are used as training and testing data in landslide susceptibility models [10] and1[11] . The main objective of landslide susceptibility models is to identify high possibility areas for landslides based on the factors and their relationships [7] . International Journal of Geoinformatics, Vol.19, No. 7, July, 2023 ISSN: 1686-6576 (Printed) | ISSN 2673-0014 (Online) | © Geoinformatics International 34 Landslide susceptibility models can be based on several groups of methods, such as multi-criteria decision-making methods based on expert opinions, statist

FAQs

What is the purpose of landslide susceptibility mapping in this study?

It classifies areas at risk of landslides by analyzing the spatial distribution and relationships of conditioning factors.

Which three models are compared for the landslide susceptibility model?

Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Random Forest (RF).

How were the data sets and conditioning factors used for model evaluation?

Landslide classification points were split into a 70% training set and a 30% testing set, and 13 conditioning factors were used to evaluate influences.

Aug 5, 2026

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