landslide susceptibility mapping using frequency ratio

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). In this study, the FR method was used for building a landslide susceptibility map along NH-6. Then the landslide susceptibility map was compared with known landslide locations and verified. AU - Aditian, Aril. Datasets utilized in the model included a complete-island landslide . Based on these definitions, landslide susceptibility is the probability of potential occurrence of slope failure, given a set of geo-environmental conditions. Whereas, detailed landslide susceptibility maps are rarely available on this road. To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. D. Sudiana and E. Diasmara, 'Analisis Indeks Vegetasi menggunakan Data Satelit', Semin. An innovative, fast . Landslides are common natural disasters in Bogor, Indonesia, triggered by a combination of factors including slope aspect, soil type and bedrock lithology, land cover and land use, and hydrologic conditions. Several researchers have used Frequency ratio model on landslide studies (Bahrain et al. The combined model outperforms the best results (AUC=0.953 and Kappa=0.79) when compared to the single models (AUC: 0.944, 0.929 and 0.91, and Kappa: 0.73, 0.72 and 0.65 for ANN, LR and FR, respectively) equipped with the high potential in mapping landslide susceptibility. This article emphasizes landslide susceptibility mapping along Ghat road of Kolli hills, Tamil Nadu, India, using frequency ratio, relative effect and fuzzy gamma operator models with the help of remote sensing data and GIS technique. By applying the statistically based frequency ratio and logistic regression models in Lantau Island, Hong Kong, this study attempts to add extra value to the literature of evaluating their 'prediction rate' (rather than 'success rate') for landslide susceptibility mapping in a temporal . Landslide susceptibility mapping (LSM) plays a crucial role in the evaluation and extenuation of risk. India, using frequency ratio, relative effect and fuzzy gamma operator models with the help . The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes. Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district . As it observed from Table 3, the landslide susceptibility map produced using the frequency ratio model, very low and low landslide susceptibility classes cover 22.7% and 30.8% of the total . This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron . 2015; Pham et al. Frequency ratio method was used to evaluate the susceptibility, and shows 89.53% accuracy upon mapping to the actual landslides. Key words: Frequency Ratio (FR), landslide causative factors, landslide susceptibility, East-West 3.4 Landslide Susceptibility Mapping The Frequency Ratio (FR) method is one of the simple probabilistic models [15], based on the spatial relationships between the landslide inventory and landslide influencing factors [7]. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Of late, a high-level convolutional neural network (CNN) has been applied with the . Validation of the Frequency Ratio Model Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF . Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages 289-302. The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF . A landslide inventory was built by historical records; satellite images; and extensive field surveys. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat . a Based on frequency ratio model, b based on fuzzy gamma operator model (c = 0.99) susceptibility map based on the frequency ratio model determined 12.73 % of the total area as very low susceptibility, 26 % of area as low . S. Moradi and M. Rezaei, 'A GIS-based comparative study of the analytic hierarchy process , bivariate statistics and frequency ratio methods for landslide susceptibility mapping in part of the Tehran metropolis , Iran', vol. 3.4.1. . landslide susceptibility mapping (lsm) comprises the collection of spatial data related to factors having an influence on the emergence of landslides in the selected research area, determination of landslide susceptibility using the relation between the existing landslides in the inventory map and the factors, and validation of the findings … The application of the Frequency Ratio (FR) method and GIS allows for faster, cheaper, more efficient and easily updated landslide susceptibility area determination. KW - Artificial neural network. The investigation involved systematic collection of data pertaining to the geological, structural and geomorphic variables. Y1 - 2018/10/1 4. Or landslide susceptibility measures the degree to which a terrain can be affected by future slope movement (Reichenbach et al. The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Subsequently; a geospatial database was established based . AU - Shinohara, Yoshinori. Frequency ratio Understanding the area specific, physical conditions and processes for triggering the landslides is of significant importance to evaluate the probability of landslides. 2015a; Haoyuan Hong et al. curve and 84.68 % in the case of the prediction rate curve, indicating a high quality susceptibility map obtained from the FR model. Landslide susceptibility map developed using the FR model in the study area is shown in Fig. The locations of 157 landslides were identified using Google Earth or extracted from archived data, from which, 22 rockslides were eliminated from the data-set due to their different conditions. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. We would like to show you a description here but the site won't allow us. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison : A case study from Kat landslides (Tokat-Turkey). DOI: 10.1016/j.jafrearsci.2020.103795 Corpus ID: 213680982; Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia In this paper, landslide susceptibility maps of Kat County (Tokat-Turkey) were prepared by use of probability model (frequency ratio, FR), logistic regression (LR) and artificial neural networks. susceptibility mapping is often an almost inhibitive activity. Polat, A. Prediction rate and success rate under curves were found to be 0.884 and 0.854, respectively. T1 - Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). AU - Kubota, Tetsuya. The landslide conditioning factors considered for the study area were slope gradient, curvature, topographic wetness index and soil type. The Landslide Susceptibility Index map was prepared using GIS and it divides the study area into five zones of landslide susceptibility viz., very low, low, moderate, high and very high. The relationships, frequency ratio and logistic regression coefficient, were overlaid to determine each factor's rating for landslide susceptibility mapping. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. As a result, it is critical to figure out how DEM spatial resolution affects the landslide susceptibility model. The landslide susceptibility maps were prepared by calculating the relationship between the landslide causative parameters with landslide areas using a frequency ratio model. The main purpose of this paper is to explore a . These landslide susceptibility maps were verified and compared using the existing landslide inventory data. In this study, we have used Frequency Ratio (FR) for landslide susceptibility mapping. Present research is Landslide Susceptibility investigation which is performed through indirect - quantitative approach by statistical scheme of frequency ratio (FR) and machine intelligence technique of logistic regression (LR) to make a map of landslide vulnerability or Susceptibility in the district of Nilgiri in Tamil Nadu, India. The landslide susceptibility map was classified as very. Landslide Susceptibility Mapping Using GIS-based Information Value and Frequency Ratio Methods in Gindeberet area, West Shewa Zone, Oromia Region, Ethiopia Alembante Genene (MSc) 1* (First author) Department of Geology, College of Applied Sciences,1 Addis Ababa Science and Technology University, Addis Ababa, Ethiopia; P.O.Box: 16417 . Landslides are one of the most common and devastating natural hazards worldwide, which cause injuries to life and damage to properties, infrastructures leading to high-cost maintenance. The landslide 123 Nat Hazards Fig. 1, pp. The 135 remaining landslides were randomly divided To date, a large number of machine learning approaches have been applied to LSM. These mountains are the landslide susceptibility map quantitatively, the frequency ratio method was implemented using GIS techniques. In the Bogor area, slopes with volcanic lithologies are more susceptible to failure. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope . This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related. Digital elevation model (DEM) was first constructed using GIS software. Experimental results shows that slope of 40°-50°, elevation of more than 1000 meters, areas close to roads and average rain of more than 200 mm are the main contributor to landslides. The Study Area 95 The study area, Shahpur valley lies in the Hindu Raj Mountains. Engineering Geology. frequency ratio [3-5], weights of evidence [4, 6, 7], logistic regression [6, 8, 9]. Google Scholar; Lippmann, 1987. . Paired comparison matrices were created to determine. Introduction According to the United Nations High Commissioner for Refugees (UNHCR) 2016 report, nearly 36 million people were displaced by natural hazard related disasters in 2009.The number is projected to increase to at least 50 million by 2050 ( UNHCR, 2016 ). However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The Landslide Susceptibility Zonation (LSZ) map is graded into five classes vary low, low, moderate, high and very high using natural break method of Jenks available in ArcGIS. The purpose of . evidence (WofE), fuzzy logic, and frequency ratio (FR) methods for landslide susceptibility mapping. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat . The map could be used by decision makers as basic information for slope management and land use planning. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. Landslide Susceptibility Mapping Using Bivariate Frequency Ratio Model and Geospatial Techniques . Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope . The landslide has happened quite frequently on National Highway 6 (NH6) and causes damage to both property and life every year. This paper documents the landslide susceptibility mapping in the Garuwa sub-basin, East Nepal using frequency ratio method. proposed for landslide susceptibility mappings. A locked padlock) or https:// means you've safely connected to the .gov website. GIS was used for the management and manipulation of spatial data. v71. It can be seen from Figure 5 and Table 10 that the very low and the low susceptibility areas had an area of 84.25 km 2 and 81.89 km 2 , accounting for 23.14% and 22.49% of the total . The research aims to determine the most influential erosion causative factor in Mamasa Sub-watershed by building a landslide susceptibility map using the frequency ratio method. Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. Fig. A higher The Frequency Ratio method was applied to determine the relationship between the attribute classes of the parameters and the landslide events. The main purpose of this paper is to explore a . To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. KW - Landslide . Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. The contributing factors to lanslide susceptibility are land closure, lithology, curve, slope direction aspect, slope, precipitation, fault distance, and river distance. Landslides are one of the most widespread disasters and threaten people's lives and properties in many areas worldwide. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison : A case study from Kat landslides (Tokat-Turkey). 2015; Pir- 2018 ). 2014; Meten et al. The five scripts (frequency_ratio.py, information_value.py, Logistic_Regression.py, randomforest.py, and MLP.py) are used to create landslide susceptibility map with the methods of Frequency Ratio (FR), Information Value (IV), Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). (2020) prepared susceptibility maps for floods, forest fires, and landslides individually for four different watersheds located in Fars, Iran using the RF model. (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh Shamima Ferdousi Sifa, Tonoy Mahmud, Maria Abdullah Tarin and Dewan Mohammad Enamul Haque . The correlation of each parameter with landslide occurrences was then measured by using probabilistic based frequency ratio method and landslide susceptibility map was produced. landslides were calculated using frequency ratio and logistic regression models. (2021) investigated floods, landslides, and gullies by means of the maximum entropy machine . DOI: 10.1016/J.GEOMORPH.2018.06.006 Corpus ID: 135413705; Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia Satellite images ; and extensive field surveys sensitive information only on official secure. A large number of machine learning approaches have been applied susceptibility assessment ( LSA ) as an essential step landslide. Was compared with known landslide locations and verified prediction based... < /a Abstract! 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