scholarly journals Preliminary Study of Landslide Susceptibility Modeling with Random Forest Algorithm Using R (Case Study: the Cisangkuy Sub-watershed)

2021 ◽  
Vol 936 (1) ◽  
pp. 012015
Author(s):  
S Sukristiyanti ◽  
K Wikantika ◽  
I A Sadisun ◽  
L F Yayusman ◽  
E Soebowo
Keyword(s):  
Random Forest ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Landslide Susceptibility Mapping ◽  
Random Forest Algorithm ◽  
Visual Interpretation ◽  
Susceptibility Modeling ◽  
Initial Measure

Abstract Landslide susceptibility mapping is an initial measure in the landslide hazard mitigation. This study aims to evaluate landslide susceptibility in the Cisangkuy Sub-watershed, a part of Bandung Basin. Twenty-seven landslide variables were involved in this modeling derived from various data sources. As a target, 25 landslide polygons obtained through a visual interpretation of Google Earth timeseries images and 33 landslide points obtained from a field survey and an official landslide report, were used as landslide inventory data. All spatial data were prepared in the same cell size referring to the highest spatial resolution of data involved in this modeling, i.e., 8.34 m. Fifty-eight (58) landslide locations covering an area of 0.87 Ha are equivalent to 1040 cells in the raster format. In total, 2040 samples consisting of landslides and non-landslides with the same ratio, were trained using random forest algorithm. Non-landslides were sampled randomly from landslide-free cells. This modeling was executed using R environment. In this study, the result was two labels, susceptible and non-susceptible. This model provided an excellent performance, its accuracy reached 98.56%. This research needs an improvement to provide a probability that has a range of 0 to 1 to show the level of landslide susceptibility.

Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil

2019 ◽  
Vol 99 (2) ◽  
pp. 1049-1073 ◽  
Author(s):  
Guilherme Garcia de Oliveira ◽  
Luis Fernando Chimelo Ruiz ◽  
Laurindo Antonio Guasselli ◽  
Claus Haetinger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
River Basin ◽  
Landslide Susceptibility ◽  
Southern Brazil ◽  
Susceptibility Modeling ◽  
Artificial Neural

Sample Position Affect Landslide Susceptibility Models in Hotspot Area of Nam Ma Basin, Lai Chau, Viet Nam

2020 ◽  
Author(s):  
Van Trung Chu ◽  
Shou-Hao Chiang ◽  
Tang-Huang Lin
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
State Of The Art ◽  
Google Earth ◽  
The State ◽  
Viet Nam ◽  
Susceptibility Modeling ◽  
Sample Position

<p>The arm of this study to analyze the effect of landslide sample position with point-based approaches for landslide susceptibility modeling which were conducted in the hotspot of the land sliding area located downstream of Nam Ma watershed (Sin Ho, Lai Chau, Viet Nam). Seven hundred fifty-nine landslide polygons that occurred in 2018 were mapped by using google earth integrated with field survey and 84 landslide points extracted from the inventory map conducted in 2013. The state-of-the-art sampling techniques and sample partition approach were applied to produce three subsets of training and testing point-based. Such as the highest position point within landslide polygon (SUB1), the centroid of landslide polygon (SUB2) and the point at the highest position within the seed cell area of the landslide polygon (SUB3). Along with that, the optimal strategy in selecting non-landslide samples was also applied and was first explicitly introduced in this study. Besides, multiple landslide conditioning factors were considered including topographic, geomorphological and hydrological groups. Especially beside of commonly used factors such as slope, elevation, curvature, land use land cover, aspect, etc. the unusual variables also considered such as high above the nearest drainage (HAND - the state-of-the-art terrain) or time series disturbance of land surface index was the first use in this study for landslide analysis and other cutting-edge data processing were proposed in this research arming to optimize the most vital part of whole procedure. The next stage of the analysis is landslide susceptibility modeling. In order to have a more objective judgment about the main issue mentioned above, instead of using only one model, we applied three different models namely Random forest (RF), Logistic regression (LR) and Decision tree (DT) to perform three kinds of scenarios by difference subsets of landslides with five folds of training phase. Subsequently, to compare the abilities of those cases, the model performance was assessed by using the area under the receiver operating characteristic curve both in model success rate (AUCSR) and model predictive rate (AUCPR). Finally, based on the results of this study, all three models performed consistent with three scenarios means the SUB2 and SUB3 are quite similar and much higher than the contribution of SUB1. And the model ability analysis indicated that RF can obtain higher accuracy following by LR and the lowest is DT.</p><p><strong>Keywords:</strong> Sample position, Landslide Susceptibility, Logistic regression, Random forest, Decision tree, Viet Nam.</p>

scholarly journals LANDSLIDE SUSCEPTIBILITY ASSESSMENT THROUGH FUZZY LOGIC INFERENCE SYSTEM (FLIS)

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 355-360 ◽  
Author(s):  
T. Bibi ◽  
Y. Gul ◽  
A. Abdul Rahman ◽  
M. Riaz
Keyword(s):  
Fuzzy Logic ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Complex Nature ◽  
Susceptibility Map ◽  
Likelihood Ratios ◽  
Regular Feature ◽  
Inference System

Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

scholarly journals Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

2013 ◽  
Vol 1 (2) ◽  
pp. 957-1000 ◽  
Author(s):  
M. Fressard ◽  
Y. Thiery ◽  
O. Maquaire
Keyword(s):  
Landslide Susceptibility ◽  
Roc Curves ◽  
Statistical Modelling ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Accuracy ◽  
Multivariate Statistical ◽  
The Impact ◽  
Modelling Methods

Abstract. The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

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