scholarly journals Implementation of Landslide Susceptibility Model Using Machine Learning for Semi Detailed Map Scale in Mountainous Region of Java Island

2021 ◽  
Vol 873 (1) ◽  
pp. 012087
Author(s):  
Imam A. Sadisun ◽  
Rendy D. Kartiko ◽  
Indra A. Dinata
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Landslide Occurrence ◽  
Related Data ◽  
Susceptibility Modeling ◽  
Digital Elevation ◽  
Susceptibility Model ◽  
Method Yield ◽  
Map Scale ◽  
Robust Prediction

Abstract Landslide susceptibility modeling using neural network (ANN) are applied to semi detailed volcanic-sedimentary water catchment. Annually landslide occurred in catchment area frequently in unconsolidated and weathered material combined with uncertainty in rainfall pattern that complicated landslide occurrence. Data used for analysis including landslide inventory, geology, digital elevation related data, distance to stream, and several other available data. Results show that machine learning method yield fair result data based on evaluation on Area under Curve (AUC). Thus, it can be suggested that machine learning methods for landslide susceptibility model could still be develop to produce robust prediction model with different characterization of parameter data and machine learning parameters.

scholarly journals GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 857-876
Author(s):  
Sk Ajim Ali ◽  
Farhana Parvin ◽  
Jana Vojteková ◽  
Romulus Costache ◽  
Nguyen Thi Thuy Linh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Susceptibility Modeling

scholarly journals Statistical and expert-based landslide susceptibility modeling on a national scale applied to North Macedonia

2019 ◽  
Vol 11 (1) ◽  
pp. 750-764
Author(s):  
Ivica Milevski ◽  
Slavoljub Dragićević ◽  
Matija Zorn
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Analytical Hierarchy Process ◽  
Landslide Susceptibility ◽  
Cross Validation ◽  
Landslide Inventory ◽  
National Scale ◽  
Landslide Occurrence ◽  
Susceptibility Modeling ◽  
Hierarchy Process

Abstract This article presents a Geographic Information System (GIS) assessment of Landslide Susceptibility Zonation (LSZ) in North Macedonia. Because of the weak landslide inventory, statistical method (frequency ratio) is combined with Analytical Hierarchy Process (AHP). In this study, lithology, slope, plan curvature, precipitations, land cover, distance from streams, and distance from roads were selected as precondition factors for landslide occurrence. There are two advantages of the approach used. The first is the possibility of comparing of the results and cross-validation between the statistical and expert based methods with an indication of the advantages and drawbacks of each of them. The second is the possibility of better weighting of precondition factors for landslide occurrence, which can be useful in cases of weak landslide inventory. The final result shows that in the case of weak landslide inventory, LSZmap created with the combination of both models provide better overall results than each model separately.

scholarly journals A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 118 ◽  
Author(s):  
Viet-Hung Dang ◽  
Nhat-Duc Hoang ◽  
Le-Mai-Duyen Nguyen ◽  
Dieu Tien Bui ◽  
Pijush Samui
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Conditioning Factors ◽  
Susceptibility Modeling

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

CATENA ◽  
2019 ◽  
Vol 175 ◽  
pp. 203-218 ◽  
Author(s):  
Binh Thai Pham ◽  
Indra Prakash ◽  
Sushant K. Singh ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Approaches ◽  
Susceptibility Modeling ◽  
Ensemble Techniques ◽  
Hybrid Machine

scholarly journals Automatic delineation of geomorphological slope units with <tt>r.slopeunits v1.0</tt> and their optimization for landslide susceptibility modeling

2016 ◽  
Vol 9 (11) ◽  
pp. 3975-3991 ◽  
Author(s):  
Massimiliano Alvioli ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Mauro Rossi ◽  
Francesca Ardizzone ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Central Italy ◽  
Optimization Approach ◽  
Optimal Parameters ◽  
Susceptibility Modeling ◽  
Digital Elevation ◽  
Terrain Units ◽  
Elevation Model ◽  
Reliable Software ◽  
Selection Of

Abstract. Automatic subdivision of landscapes into terrain units remains a challenge. Slope units are terrain units bounded by drainage and divide lines, but their use in hydrological and geomorphological studies is limited because of the lack of reliable software for their automatic delineation. We present the r.slopeunits software for the automatic delineation of slope units, given a digital elevation model and a few input parameters. We further propose an approach for the selection of optimal parameters controlling the terrain subdivision for landslide susceptibility modeling. We tested the software and the optimization approach in central Italy, where terrain, landslide, and geo-environmental information was available. The software was capable of capturing the variability of the landscape and partitioning the study area into slope units suited for landslide susceptibility modeling and zonation. We expect r.slopeunits to be used in different physiographical settings for the production of reliable and reproducible landslide susceptibility zonations.

scholarly journals AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 713-720
Author(s):  
G. Karakas ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Natural Phenomenon ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Aerial Photogrammetry ◽  
Prone Area ◽  
Using Data

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.

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