# Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

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## Abstract

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## 1. Introduction

^{2}(198,120 mi

^{2}). Most of Thailand’s terrain is the high mountainous area in the North, an upland plateau in the Northeast, a central plain in the middle and the sea in the South. Thailand’s climate is divided into three seasons which are the rainy, winter, and summer seasons. Large landslides have occurred in Thailand every 3–5 years [22]. Research on landslides and landslide forecasts in Thailand have constantly been developed. Various prediction approaches have been employed for estimating landslides risk such as the GIS, remote sensing techniques, statistical methods, or machine learning models. Phetchabun Province used GIS and remote sensing techniques in 2006 [23]. In 2010, a weighting factor and geotechnical methods were used to estimate the safety factor scores, then generate a landslide susceptibility map of Phuket Island by using APIs. Some studies have used statistical methods to predict landslide probability levels. A landslide probability model under multiple global climate models (GCMs) was applied to project the probability of landslide occurrences in Thailand in 2014 and used a shallow landslide instability prediction model (SLIP) to foretell the risks of landslides based on rainfall triggering factor in 2018 [24,25]. Almost all research predicts the likelihood of landslides for an area at the provincial, district, or sub-district level. However, the landslide prediction of smaller areas, at the village level or smaller, have not been largely presented. Therefore, the smart GIS web application of landslide-prediction risk is an important challenge for this country.

## 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 km
^{2}.

#### 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

^{th}input at the l

^{th}layer e.g., l = 1, 2, 3, and so on. The term ${w}_{ij}^{\left(l\right)}$ indicates the weight of the l

^{th}layer connecting the j

^{th}node to the i

^{th}node in the next layer, ${b}_{i0}^{\left(l\right)}$ is the bias term of the l

^{th}layer connecting to the i

^{th}node, and ${f}^{\left(l\right)}\left(\xb7\right)$ is the activation function of the l

^{th}layer. The rectified linear units (ReLU) and the Sigmoid function are the two types of the activation functions that are used in this research. The ReLU and sigmoid function can be expressed as illustrated in Equations (3) and (4), respectively.

#### 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:- (1.1)
- generate grid-based maps of 3 historical landslide occurs of Study areas given by Aerial photographs, Google Satellite, and site survey photographs.
- (1.2)
- identify 5 types of land cover from Google Satellite and Aerial photographs and render land cover digital map.
- (1.3)
- determine physiographic by using Aerial photographs and site survey photographs.
- (1.4)
- compute and render elevation and slope from Aerial photographs and site survey photographs.
- (1.5)
- fetch historical rainfall data from Department of Water Resources, Ministry of Natural Resources and Environment, Thailand.
- (1.6)
- export all attributes into the CSV files.
(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

- Batar, A.K.; Watanabe, T. Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions. ISPRS Int. J. Geo-Inf.
**2021**, 10, 114. [Google Scholar] [CrossRef] - Youssef, A.M.; Al-Kathery, M.; Pradhan, B. Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci. J.
**2015**, 19, 113–134. [Google Scholar] [CrossRef] - Huang, F.; Ye, Z.; Huang, J.; Jiang, S.; Chang, Z.; Chen., J. Uncertainty study of landslide susceptibility prediction considering different attribute interval numbers of environmental factors and different data-based models. CATENA
**2021**, 202, 105250. [Google Scholar] [CrossRef] - Jiang, S.H.; Huang, J.; Qi, X.H.; Zhou, C.B. Efficient probabilistic back analysis of spatially varying soil parameters for slope reliability assessment. Eng. Geol.
**2020**, 271, 105597. [Google Scholar] [CrossRef] - Liu, J.; Duan, Z. Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan area, China. Entropy
**2018**, 20, 868. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Yang, J.; Song, C.; Yang, Y.; Xu, C.; Guo, F.; Xie, L. New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China. Geomorphology
**2019**, 324, 62–71. [Google Scholar] [CrossRef] - Pourghasemi, H.; Moradi, H.; Aghda, S.F. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat. Hazards
**2013**, 69, 749–779. [Google Scholar] [CrossRef] - Battineni, G.; Sagaro, G.G.; Nalini, C.; Amenta, F.; Tayebati, S.K. Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods. Machines
**2019**, 7, 74. [Google Scholar] [CrossRef] [Green Version] - Bui, T.D.; Pham, B.T.; Nguyen, Q.P.; Hoang, N.-D. Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: A case study in Central Vietnam. Int. J. Digit. Earth
**2016**, 9, 1077–1097. [Google Scholar] - Chang, Z.; Du, Z.; Zhang, F.; Huang, F.; Chen, J.; Li, W.; Guo, Z. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sens.
**2020**, 12, 502. [Google Scholar] [CrossRef] [Green Version] - Chen, W.; Pourghasemi, H.R.; Naghibi, S.A. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bull. Eng. Geol. Environ.
**2018**, 77, 647–664. [Google Scholar] [CrossRef] - Sepulveda, N.E.; Sinha, J. Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines. Machines
**2020**, 8, 66. [Google Scholar] [CrossRef] - Parathai, P.; Tengtrairat, N.; Woo, W.L. Sound Events Separation and Recognition using L1-Sparse Complex Nonnegative Matrix Factorization and Multi-Class Mean Supervector Support Vector Machine. In Proceedings of the 2nd International Conference Information Technology, Bangkok, Thailand, 2–3 November 2017. [Google Scholar]
- Huang, F.; Cao, Z.; Jiang, S.; Zhou, C.; Huang, J.; Guo, Z. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides
**2020**, 17, 2919–2930. [Google Scholar] [CrossRef] - Nhu, V.-H.; Shirzadi, A.; Shahabi, H.; Singh, S.K.; Al-Ansari, N.; Clague, J.J.; Jaafari, A.; Chen, W.; Miraki, S.; Dou, J.; et al. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. Int. J. Environ. Res. Public Health
**2020**, 17, 2749. [Google Scholar] [CrossRef] [PubMed] - Roy, A.C.; Islam, M.M. Predicting the Probability of Landslide using Artificial Neural Network. In Proceedings of the The 5th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 26–28 September 2019; pp. 874–879. [Google Scholar]
- Zhu, L.; Huang, L.; Fan, L.; Huang, J.; Huang, F.; Chen, J.; Zhang, Z.; Wang, Y. Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network. Sensors
**2020**, 20, 1576. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Xie, P.; Zhou, A.; Chai, B. The Application of Long Short-Term Memory (LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides. IEEE Access
**2019**, 7, 54305–54311. [Google Scholar] [CrossRef] - Jiang, H.; Li, Y.; Zhou, C.; Hong, H.; Glade, T.; Yin, K. Landslide Displacement Prediction Combining LSTM and SVR Algorithms: A Case Study of Shengjibao Landslide from the Three Gorges Reservoir Area. Appl. Sci.
**2020**, 10, 7830. [Google Scholar] [CrossRef] - Koh, B.H.D.; Lim, C.L.P.; Rahimi, H.; Woo, W.L.; Gao, B. Deep Temporal Convolution Network for Time Series Classification. Sensors
**2021**, 21, 603. [Google Scholar] [CrossRef] [PubMed] - Hamad, R.A.; Yang, L.; Woo, W.L.; Wei, B. Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition. Appl. Sci.
**2020**, 10, 5293. [Google Scholar] [CrossRef] - Fowze, J.S.M.; Bergado, D.T.; Soralump, S.; Voottipreux, P.; Dechasakulsom, M. Rain-triggered landslide hazards and mitigation measures in Thailand: From research to practice. J. Geotexmem
**2012**, 30, 50–64. [Google Scholar] [CrossRef] - Yumuang, S. Phetchabun province, central Thailand. Environ. Geol.
**2006**, 51, 545–564. [Google Scholar] [CrossRef] - Inoue, N.; Ono, K.; Komori, D.; Kazama, S. Projection of extreme-rainfall-induced landslide in Thailand using three Global Climate Models. In Proceedings of the 19th IAHR-APD Congress 2014, Hanoi, Vietnam, 21–24 September 2014. [Google Scholar]
- Komori, D.; Rangsiwanichpong, P.; Inoue, N.; Ono, K.; Watanabe, S.; Kazama, S. Distributed probability of slope failure in Thailand under climate change. Clim. Risk Manag.
**2018**, 20, 126–137. [Google Scholar] [CrossRef] - Koh, B.H.D.; Woo, W.L. Multiview Temporal Ensemble for Classification of Non-Stationary Signals. IEEE Access
**2019**, 7, 32482–32491. [Google Scholar] [CrossRef] - Nobaew, B. Three-Dimensional Landslide Model For Predicting Affected Area With Particle Flow Simulation. J. Inf. Sci. Technol.
**2010**, 1, 11–16. [Google Scholar] - Hungr, O.; Leroueil, S.; Picarelli, L. The Varnes classification of landslide types, an update. Landslides
**2014**, 11, 167–194. [Google Scholar] [CrossRef] - Ono, K.; Kazama, S.; Ekkawatpanit, C. Assessment of rainfall-induced shallow landslides in Phetchabun and Krabi provinces, Thailand. Nat. Hazards
**2014**, 74, 2089–2107. [Google Scholar] [CrossRef] - Dai, F.C.; Lee, C.F. Frequency–volume relation and prediction of rainfall-induced landslides. Eng. Geol.
**2001**, 59, 253–266. [Google Scholar] [CrossRef] - DiGangi, E.A.; Hefner, J.T. Research Methods in Human Skeletal Biology; Academic Press: Waltham, MA, USA, 2013. [Google Scholar]
- Schein, A.I.; Ungar, L.H. Active learning for logistic regression: An evaluation. Mach. Learn.
**2007**, 68, 235–265. [Google Scholar] [CrossRef] - Chen, Y.-Y.; Lin, Y.-H.; Kung, C.-C.; Chung, M.-H.; Yen, I.-H. Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes. Sensors
**2019**, 19, 2047. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Woo, W.L. Human-Machine Co-Creation in the Rise of AI. IEEE Instrum. Meas. Mag.
**2020**, 23, 2020. [Google Scholar] [CrossRef] - Parathai, P.; Tengtrairat, N.; Woo, W.L.; Abdullah, M.A.M.; Rafiee, G.; Alshabrawy, O. Efficient Noisy Sound Separation and Event Classification using Single Microphone with Adaptive-Sparse Complex-valued Matrix Factorization and OvsO SVM. Sensors
**2020**, 20, 4368. [Google Scholar] [CrossRef] - Tengtrairat, N.; Woo, W.L. Blind 3D Sound Source Direction using Stereo Microphones based on Time-Delay Estimation and Polar-Pattern Histogram. In Proceedings of the 2nd International Confefence Information Technology, Bangkok, Thailand, 2–3 November 2017. [Google Scholar]
- Parathai, P.; Tengtrairat, N.; Woo, W.L.; Gao, B. Single-Channel Signal Separation using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization. Circuits Syst. Signal Process.
**2019**, 38, 5786–5816. [Google Scholar] [CrossRef] - Abraham, M.T.; Satyam, N.; Pradhan, B.; Alamri, A.M. Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas. Water
**2020**, 12, 804. [Google Scholar] [CrossRef] [Green Version] - Jing, L.; Gulcehre, C.; Peurifoy, J.; Shen, Y.; Tegmark, M.; Soljacic, M.; Bengio, Y.A. Gated Orthogonal Recurrent Units: On Learning to Forget. Neural Comput.
**2019**, 31, 765–783. [Google Scholar] [CrossRef] - Zhang, Y.; Tang, J.; He, Z.; Tan, J.; Li, C. A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide. Nat. Hazards
**2021**, 105, 783–813. [Google Scholar] [CrossRef] - Rodríguez, R.; Rodríguez, J.-V.; Woo, W.L.; Wei, B.; Pardo-Quiles, D.-J. A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus. Appl. Sci.
**2021**, 11, 1742. [Google Scholar] [CrossRef] - Kadavi, P.R.; Lee, C.-W.; Lee, S. Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens.
**2018**, 10, 1252. [Google Scholar] [CrossRef] [Green Version] - Ruan, L.; Gao, B.; Wu, S.; Woo, W.L. DeftectNet: Joint Loss Structured Deep Adversarial Network for Thermography Defect Detecting System. Neurocomputing
**2020**, 417, 441–457. [Google Scholar] [CrossRef] - Cui, W.; He, X.; Yao, M.; Wang, Z.; Li, J.; Hao, Y.; Wu, W.; Zhao, H.; Chen, X.; Cui, W. Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM. ISPRS Int. J. Geo-Inf.
**2020**, 9, 194. [Google Scholar] [CrossRef] [Green Version] - Wang, H.; Zhang, L.; Luo, H.; He, J.; Cheung, R.W.M. AI-powered landslide susceptibility assessment in Hong Kong. Eng. Geol.
**2021**, 288, 106103. [Google Scholar] [CrossRef] - Siami-Namini, S.; Tavakoli, N.; Namin, A.S. The Performance of LSTM and BiLSTM in Forecasting Time Series. In Proceedings of the IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9–12 December 2019; pp. 3285–3292. [Google Scholar]
- Moresi, F.V.; Maesano, M.; Collalti, A.; Sidle, R.C.; Matteucci, G.; Scarascia Mugnozza, G. Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy. Geosciences
**2020**, 10, 309. [Google Scholar] [CrossRef] - Golovko, D.; Roessner, S.; Behling, R.; Wetzel, H.-U.; Kleinschmit, B. Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan. Remote Sens.
**2017**, 9, 943. [Google Scholar] [CrossRef] [Green Version] - An, K.; Kim, S.; Chae, T.; Park, D. Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources. Sustainability
**2018**, 10, 293. [Google Scholar] [CrossRef] [Green Version] - Yousefi, S.; Pourghasemi, H.R.; Emami, S.N.; Pouyan, S.; Eskandari, S.; Tiefenbacher, J.P. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci. Rep.
**2020**, 10, 12144. [Google Scholar] [CrossRef] - Wang, H.-H. Design of Monitoring System for Uneven Settlement of Soft Soil Foundation based on Web GIS. In Proceedings of the IEEE International Confefence on Industrial Application of Artificial Intelligence (IAAI), Harbin, China, 25–27 December 2020; pp. 149–154. [Google Scholar]
- Iqbal, M.S.; Ali, A.; Naseem, A.; Majeed, R. A Flexible Highly Configurable System Architecture for Geographical Information System. In Proceedings of the IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 5–7 November 2020; pp. 1–6. [Google Scholar]
- Chow, T.E. The Potential of Maps APIs for Internet GIS Applications. Trans. GIS
**2008**, 12, 179–191. [Google Scholar] [CrossRef] - Qi, Q.; Cao, J. Investigating the Evolution of Web API Cooperative Communities in the Mashup Ecosystem. In Proceedings of the IEEE International Conference on Web Services (ICWS), Beijing, China, 19–23 October 2020; pp. 413–417. [Google Scholar]
- Hu, B.; Gao, B.; Woo, W.L. A Lightweight Spatial and Temporal Multi-feature Fusion Linked Self-Attention Network for Defect Detection. IEEE Trans. Image Process.
**2021**, 30, 472–486. [Google Scholar] [CrossRef] [PubMed] - Kaderuppan, S.; Wong, E.W.L.; Sharma, A.; Woo, W.L. Smart Nanoscopy: A Review on Computational Approaches to achieve Super-resolved Optical Microscopy. IEEE Access
**2020**, 8, 214801–214831. [Google Scholar] [CrossRef]

**Figure 2.**Landslide on 4 September 2017 at Pha Dua Village Community (Study Area A). Ref. https://www.thairath.co.th/news/local/north/1062644 (accessed on 30 May 2021).

**Figure 3.**Landslide on 21 August 2018 at Mai Salong Nok (Study Area B). Ref. https://mgronline.com/local/detail/9610000083606 (accessed on 30 May 2021).

**Figure 4.**Landslide on 31 July 2018 at Phu Chifa, (Study Area C). Ref. https://mgronline.com/local/detail/9610000075917 (accessed on 30 May 2021).

**Figure 11.**The 4-day cumulative rainfall of 3 rainfall monitoring stations in 2017 of Study Area A and B.

**Figure 24.**Accuracy of various numbers of batch sizes, epochs, and nodes of the 3rd hidden layer ANN3.

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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Tengtrairat, 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