Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models
Abstract
:1. Introduction
2. Methodology
2.1. Study Area
2.2. Instability Landslide Factors
- Determine the center of the study area based on an EPSG coordinate system.
- Generate based map by using QGIS tools.
- Segment the area into grid cells where a grid size is 0.25 km2.
2.2.1. Land Cover Geographic Information
- Annotate each type of land cover by using polygon tools throughout the map.
- Generate land cover dataset by exporting land cover layer into a Shape file.
2.2.2. Physiographic (Soil Types) Geographic Information
2.2.3. Elevation Geographic Information
- Create Raster layer by using DEM. Raster data is like any image that depicts various properties of objects in the real world.
- Generate contouring and elevation by using Vector > Geometry tools > Centroids, set interval between contour line, and fetching elevation values from Raster layer via Sample Raster Values tools.
2.2.4. Slope Geographic Information
2.2.5. Dynamic Rainfall Factor
2.3. Landslide-Risk Prediction Models
2.3.1. Logistic Regression
2.3.2. Artificial Neural Network
2.3.3. Gated Recurrent Units
2.3.4. LSTM
2.3.5. Bi-LSTM
2.4. Landslide-Risk Model Measurement
2.5. Proposed Automated Landslide-Risk Web GIS Application
2.5.1. QGIS for Geospatial Information
2.5.2. Automated Landslide-Risk Web GIS Application
Algorithm 1. Overview of the proposed landslide-risk prediction algorithm |
(1) Build the landslide dataset by using QGIS:
(3) Build and train the machine learning model i.e., LR, ANN, GRU, LSTM, and Bi-LSTM by using the optimal values of parameter from Step 2 given by the landslide dataset. The landslide dataset was split into a training dataset (80% of landslide dataset) and a testing dataset (20% of the landslide dataset) (4) Choose the model that delivers the best prediction performance for constructing the web application. (5) Implement and test the automatic landslide-risk prediction web GIS by using JavaScript, Node.js, and MySQL as web programing language, web development tool on server side, and database. (6) Deploy the proposed landslide-risk prediction web GIS on Google Cloud platform |
3. Experimental Results and Analysis
3.1. Determining the Optimal Parameters of Machine Learning Methods
3.2. Landslide-Predicting Performance Comparison between Studied Machine Learning Methods
3.3. Enhanced Prediction: Two-Stage Classifiers
3.4. Landslide-Risk Prediction Model Accuracy
3.5. Automated Landslide-Risk Web GIS Application
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tengtrairat, N.; Woo, W.L.; Parathai, P.; Aryupong, C.; Jitsangiam, P.; Rinchumphu, D. Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models. Sensors 2021, 21, 4620. https://doi.org/10.3390/s21134620
Tengtrairat N, Woo WL, Parathai P, Aryupong C, Jitsangiam P, Rinchumphu D. Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models. Sensors. 2021; 21(13):4620. https://doi.org/10.3390/s21134620
Chicago/Turabian StyleTengtrairat, Naruephorn, Wai Lok Woo, Phetcharat Parathai, Chuchoke Aryupong, Peerapong Jitsangiam, and Damrongsak Rinchumphu. 2021. "Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models" Sensors 21, no. 13: 4620. https://doi.org/10.3390/s21134620