GIS-based comparative study of information value and frequency ratio method for landslide hazard zonation in a part of mid-Himalaya in Himachal Pradesh

Zhang

et al. 2019

IJERPH

Driven by the pull of gravity, mass-wasting comprises all of the sedimentary processes related to remobilization of sediments deposited on slopes, including creep, sliding, slumping, flow, and fall. It is vital to conduct mass-wasting susceptibility mapping, with the aim of providing decision makers with management advice. The current study presents two individual data mining methods—the frequency ratio (FR) and information value model (IVM) methods—to map mass-wasting susceptibility in four catchments in Miyun County, Beijing, China. To achieve this goal, nine influence factors and a mass-wasting inventory map were used and produced, respectively. In this study, 71 mass-wasting locations were investigated in the field. Of these hazard locations, 70% of them were randomly selected to build the model, and the remaining 30% of the hazard locations were used for validation. Finally, a receiver operating characteristic (ROC) curve was used to assess the mass-wasting susceptibility maps produced by the above-mentioned models. Results show that the FR had a higher concordance and spatial differentiation, with respective values of 0.902 (area under the success rate) and 0.883 (area under the prediction rate), while the IVM had lower values of 0.865 (area under the success rate) and 0.855 (area under the prediction rate). Both proposed methodologies are useful for general planning and evaluation purposes, and they are shown to be reasonable models. Slopes of 6–21° were the most common thresholds that controlled occurrence of mass-wasting. Farmland terraces were mainly composed of gravel, mud, and clay, which are more prone to mass-wasting. Mass-wasting susceptibility mapping is feasible and potentially highly valuable. It could provide useful information in support of environmental health policies.

Section: Discussionmentioning

confidence: 99%

Geospatial Analysis of Mass-Wasting Susceptibility of Four Small Catchments in Mountainous Area of Miyun County, Beijing

Cao

Zhang

et al. 2019

IJERPH

“…e frequency ratio method (FR) [31,33] is a more traditional statistical analysis model. A landslide (M) is affected by many factors (n) (such as elevation, aspect, and lithology), so the frequency ratio method divides M into n classes or n grades according to certain rules.…”

Section: Frequency Ratio Methodmentioning

confidence: 99%

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Wang

Advances in Civil Engineering

2021

Landslides are one of the nature hazards causing a lot of casualties and property losses in the world. Over the last decades, many researchers have made contributions in landslide susceptibility maps using qualitative and quantitative methods. Parameters of DEM, geology, etc. are selected to analyze the mechanism of landslides. The quality of data is essential in the landslide studies, and more credible results can be obtained if the data is adequate and accurate from the wide range of parameters. The aim of this study is to evaluate the landslide susceptibility of Huangyuan County of Qinghai. Through field investigations, 100 landslide disaster locations in the study area were selected, and 11 influencing factors including elevation, slope, aspect, plane curvature, profile curvature, road distance, river distance, fault distance, stratum rock property, vegetation coverage index, and terrain humidity index were selected as the influencing factors of landslide disaster based on GIS. In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. The results show that factors that have great influence on landslides are associated with witness, and the terrain humidity index has the highest weight in the occurrences of landslide. The values of AUC indicate that the ANN model is the best evaluation model suitable for the study area and can be extremely useful for landslide hazard mitigation strategies. Based on the calculation of ANN model, three valley areas are determined with high landslide susceptibility, and necessary reinforcement measures should be taken.

“…FR method is a geospatial assessment tool that can quantify the relationship between the influencing factors and debris flows [63]. This method uses the number of pixels dominating a particular area of a region and the number of pixels affected by a debris flow.…”

Section: Frequency Ratio Methodsmentioning

confidence: 99%

Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Qin

Qiao

et al. 2020

Water

Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.