第35回日本情報地質学会講演会 講演要旨集 031-032頁 2024年

Evaluating Machine Learning Models for Landslide Susceptibility Mappingin Yen Bai Province, Vietnam
Tran TUNG LAM*, Tatsuya NEMOTO*, Truong XUAN QUANG** and Venkatesh RAGHAVAN*
Department of Geosciences, Graduate School of Science, Osaka Metropolitan University, 3-3-138
Sugimoto Sumiyoshi-ku, Osaka, 558-8585, Japan E-mail: sp22872l@st.omu.ac.jp** Vietnam National University, Hanoi School of Interdisciplinary Sciences and Arts, Hanoi, Vietnam.

*Key words: Landslide, Susceptibility, Machine Learning, Open Source

1. IntroductionAlthough landslides in Vietnam occur in all
mountainous regions, the northern mountainous area is,however, recognized as one of the regions that are mostsusceptible to landslides in the country (Dieu Tien Bui etal., 2016). According to a study by the Ministry of
Resources and Environment of Vietnam, over 70% ofcommunes in Yen Bai province face high or very highlandslide risk, particularly in Mu Cang Chai (MCC) and
Van Yen (VY) districts (Binh Thai Pham et al., 2016).
Landslide Hazard Assessment is a crucial tool inmanaging and mitigating the risks associated withlandslides. The primary product of this assessment, alandslide susceptibility map (LSM), serves as a practicaland cost-effective method for zoning areas prone tolandslides. This research focuses on implementing
Machine Learning (ML) models to create robust LSMs
that minimize geographical bias, making them applicablein diverse topographic regions, particularly in areas
significantly impacted by human activities.
2. Study Area and Methodology
2.1 Study area and data
The Mu Cang Chai (MCC) located between 21°39′N–21°50′ North latitude and 103°56′–104°23′ East Longitude
has been selected as the study area. It is located in thenorthwest of Yen Bai province of Viet Nam, covering an
area of about 1196.47 km2. Most of the study region iscovered by forests (61.76 %) (Binh Thai Pham et al., 2016).
The landslide inventory utilized the data from previous
project (Truong et al., 2023). From that, landslide andnon-landslide points are generated from recorded
landslide activities in the area.
2.2 Methodology
Utilizing data from previous research and recorded
landslide occurrences in Mu Cang Chai (MCC), a balanced
training dataset of landslide inventory is established,
incorporating in total 16 contributing factors. Principal
Component Analysis (PCA) and Pearson correlationassess the independence and correlation of these factors.
Feature importance evaluation removes the leastsignificant correlated factor. Subsequently, four machinelearning models–Random Forest (RF), Support Vector
Machine (SVM), Logistic Regression (LR), and Extreme
Gradient Boosting (XG Boost)–are deployed. Ten-foldcross-validation is employed to tune the hyperparameters,
while confusion matrices, accuracy score, Kappa score,
Receiver Operating Characteristic Curve (ROC) and Area
under the ROC Curve (AUC) are utilized to assess model
performance. After training with the MCC dataset,external validation for the ML models is conducted in the
Van Yen (VY) district with its own landslide inventory.
Additionally, the methodology is also performed in an area
within Nagaoka City, Niigata Prefecture, Japan, theareas is much smaller in size with much bigger landslide
inventory, giving much high density of training points.
This test in Nagaoka assesses the reliability of the models
in an area with entirely different topographic, hydrologic,geologic, and anthropogenic features, providing acomprehensive evaluation and identifying potential
improvements for ML models.
3. Results and Discussion
In MCC, the performance of the three models (RF,SVM, LR) is positive, but not as high as desired, with anoverall accuracy score of 0.7 and a Kappa score of 0.5.
The performance of the four models is put into
comparison in Figure 1, using the ROC and AUC for
better visualization.
When the models are validated on VY area, theRandom Forest model achieves a positive accuracy scoreof 0.8, demonstrating potential for further application.

Figure 1. ROC curves for the three models in
Mu Cang Chai.

32b)

104° 25' 02″ E
104° 25′ 02″ E

103° 53′ 07″ E

Very low

Low

Medium37° 18′ 07″ N

37° 18′ 07″ N

138° 56′ 07″ E

21° 38′ 24″ N

138° 56′ 07″ E

21° 38′ 24″ N

37° 20′ 04″ N

138° 53′ 46″ E

21° 58′ 11″ N

103° 53′ 07″ E

21° 58′ 11'' N

37° 20′ 04″ N

138° 53′ 46″ Ea)

High

Very highFigure 2. Landslide susceptibility maps for a) Mu Cang Chai; b) The study area within Nagaoka city.
datasets and is the most suited for landslide classification.
4. Summary
Accurate landslide prediction is hindered by thequality of existing inventories, which often rely on paststudies. Human activities and urbanization can alter
topography, increasing landslide susceptibility, as shownby the importance of road buffers in the PCA biplot andfeature selection process. To improve landslide mapping,future research will focus on enhancing inventory
accuracy and expanding ML model capabilities bycombining different algorithms. Additionally, extensive
data validation across diverse geographical areas will
provide further insights into model reliability.
Figure 3. ROC curves for the three models in Nagaoka.
LSM are visualized using equal interval classes acrossfive risk categories: very low, low, medium, high, and veryhigh. This classification approach allows for better
understanding of risk distribution (Figure 2a).
In Niigata, Japan, the model performs exceptionallywell, with an average accuracy of 0.94 and a Kappa scoreof 0.87. The evaluation of the ML models is in the figure
above (Figure 3).
After validating the framework in Niigata, the MLmodels trained on Nagaoka data will be applied toanother dataset in Ojiya city, Niigata Prefecture. Thevalidation process gives similar results to those obtainedin Vietnam, with Random Forest (RF) and Extreme
Gradient Boosting (XG Boost) demonstrating consistentlypositive outcomes. The landslide susceptibility map for
Nagaoka is presented in Figure 2b.
ML models offer a promising approach for precise LSM
for future landslide prediction and risk mitigationstrategies, providing a means to analyze the relationshipbetween different contributing factors. RF is shown tohave the most robustness when working with unknown

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