Probabilistic Frequency Ratio Model Research Articles

Landslide Susceptibility Mapping of Kinnaur District in Himachal Pradesh, India Using Probabilistic Frequency Ratio Model

Abstract Landslides are a recurring phenomenon in hilly areas that destroy the natural environment, causing enormous property damage and human loss every year. Geological rock structure plays an important role in the occurrence of landslides. Concentration of dolomite, phyllite, schist and quartzite make this area more susceptible to failure. Landslides are prompted with the aid of using aggregate of things like slope, soil type, lithology, land use, land cover, drainage density, relative relief, curvature, etc. The main aim of this study is to delineate landslide susceptibility zone. In the study, the primary concern was to evaluate the contribution of controlling factors to landslides and to produce a landslide susceptibility map of the examination area. This study uses the landslide susceptibility model using frequency ratio in Kinnaur district, Himachal Pradesh, India. A landslide inventory was prepared with the help of Landsat, google earth images from 2010-2020 period. Digital elevation model (DEM) was used to prepare slope, aspect, curvature, and relative relief maps. Other maps like soil, land use land cover, was prepared using LISS-iii and LISS-iv data to analyze landslide controlling factors. An aggregate of 379 landslide polygons was digitized and then divided into a preparation set (70%) with 265 polygons and a test set with 114 polygons (30%). The connection between landslides and controlling factors was statistically assessed with FR analysis. Values were utilized to create the landslide susceptibility index (LSI) and the investigation region was divided into five zones of relative landslide susceptibility which were divided as very high, high, moderate, low, and very low. The findings of the analysis were validated by calculating the area under curve (AUC), which reveals a success rate accuracy of 73.01% and a prediction rate curve accuracy of 0.73%, indicating a high-quality susceptibility map derived from the FR model.

Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs

This paper expands the previous efforts by other researchers to present a quantitative and deterministic approach for terrain analysis. This study evaluates both spatial and temporal factors contributing landslides utilizing Light Detection and Ranging (LiDAR) point clouds in conjunction with the frequency ratio model (PFR) than has previously been used in the Alborz Mountains. The study area is Marzan Abad of the Alborz Mountain in Iran. The significance of this study is the performance of a high-resolution digital elevation model (DEM) derived from LiDAR point clouds in order to provide detailed information in improving landslide susceptibility evaluation. This study discusses how we improve the quality of landslide susceptibility evaluation. We apply the PFR model to consider the effect of landslide-related factors associated with Google Earth’s high-resolution images and field observations. The LiDAR point cloud data and GIS-based analysis have allowed performing high quality ways of landslide hazard assessments using inventory dataset as compared to previous studies. We contributed an improved landslide inventory map of the Mazandaran Province. We used image elements interpretation from the available ASTER DEM (30 m), LiDAR-DEM (5 m), and the Google Earth high spatial resolution images in conjunction with the field observations. This study evaluates factors such as geology, geomorphology, landuse, soil, slope, and distance from roads and drainage to represent, manipulate, and analyze factors. Also, we evaluated the performance success of the rate curve of landslides susceptibility. The results have indicated an improved landslide susceptibility map from LiDAR-derived DEMs implementing the PFR model with 92.59% of accuracy performance as compared to the existing data and previous studies in the same region. Furthermore, this study reveals that all considering factors have relatively positive effects on the landslides susceptibility mapping in the study, however, the most effective factor on the landslide occurrence is the lithology with 13.7%.

Flowing well potential zoning at Iraqi southern and western deserts using frequency ratio and geographic information system

The objective of this study is to integrate geographic information system and bivariate frequency ratio method for the mapping of flowing well zones in the west and southwest parts of the Euphrates river basin of Iraq. Ten groundwater conditioning factors are identified as controlling factors of groundwater movement based on data availability, literature review, and expert’s opinions. The spatial association between flowing well locations and groundwater controlling factors is investigated by means of a probabilistic frequency ratio approach. Seventy percent or 148 wells from an inventory of 211 flowing wells in the study area are randomly selected for training, and the remaining 30 or 63% wells are used for validation of the probabilistic frequency ratio model. The estimated probabilistic ratio values are overlaid and summed to produce the groundwater potential index map. The results reveal that groundwater potential in 128,547 km2 or 84% of the total area is very low to low. The moderate potential zone covers an area of about 11,210 km2 or 7%, while the high and very high potential zones are found in an area of 12,982 km2 or 9% of the study area. Validation of obtaining results by means of a receiver operating characteristic technique reveals that the predictive accuracy of 94% indicating the excellent performance of the proposed approach for spatial zoning of groundwater flowing well boundary at Iraqi desert.

PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations.