Earthquake-induced landslide susceptibility evaluation based on fuzzy logic and Shannon’s entropy integrated information value models in the 2013 Minxian, China earthquake-affected area

2015 ◽  
pp. 235-240
Keyword(s):  
Fuzzy Logic ◽  
Landslide Susceptibility ◽  
Information Value ◽  
Shannon’S Entropy ◽  
Affected Area ◽  
Shannon's Entropy ◽  
China Earthquake ◽  
Susceptibility Evaluation ◽  
Integrated Information

scholarly journals COMBINING FUZZY LOGIC AND INFORMATION THEORY FOR PRODUCING A LANDSLIDE SUSCEPTIBILITY MODEL

2017 ◽  
Vol 50 (3) ◽  
pp. 1737
Author(s):  
P. Tsangaratos ◽  
I. Ilia
Keyword(s):  
Information Theory ◽  
Fuzzy Logic ◽  
Landslide Susceptibility ◽  
Shannon’S Entropy ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Entropy Index ◽  
Shannon's Entropy ◽  
Susceptibility Model

The main objective of the present study was to develop a landslide susceptibility model by combining Fuzzy logic and Information Theory in order to estimate the spatial probability of landslide manifestation, in the mountains of central Tzoumerka, Greece. Specifically, Fuzzy logic was enabled for weighting the landslide related variables based on expert knowledge and in respect to landslide susceptibility, while the Shannon’s entropy index, an index from Information Theory, was calculated to weight the significance of each landslide related variable based on the available data. The final landslide susceptibility map was produced by applying the weighted sum method. Engineering lithological units, slope angle, slope aspect, distance from tectonic features, distance from river network and distance from road network were among the six landslide related variables that were included in the landslide database used in the training phase. The landslide inventory map was constructed by interpreting aerial photographs, satellite images and field surveys and was separated into two datasets, one for training and one for validating the model. The outcomes of the validation process illustrated that the developed methodology efficiently provided the most susceptible areas and was in good agreement with the actual landslide locations. The area under the curve was estimated to be for the training and validating datasets 0.7575 and 0.7828 respectively. The produced landslide susceptibility map could be regarded from local and national authorities as a valuable mean to evaluate strategies or to prevent and mitigate the impact of landslides. Keywords: slope stability, fuzzy weighting, Shannon’s entropy index, Tzoumerka, Greece.

scholarly journals A Comparative Study of Frequency Ratio, Shannon’s Entropy and Analytic Hierarchy Process (AHP) Models for Landslide Susceptibility Assessment

2021 ◽  
Vol 10 (9) ◽  
pp. 603
Author(s):  
Sandeep Panchal ◽  
Amit Kr. Shrivastava
Keyword(s):  
Analytic Hierarchy Process ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Shannon’S Entropy ◽  
Analytic Hierarchy ◽  
Shannon's Entropy ◽  
Susceptibility Maps ◽  
Roc Area ◽  
Hierarchy Process ◽  
Landslide Susceptibility Maps

Landslide susceptibility maps are very important tools in the planning and management of landslide prone areas. Qualitative and quantitative methods each have their own advantages and dis-advantages in landslide susceptibility mapping. The aim of this study is to compare three models, i.e., frequency ratio (FR), Shannon’s entropy and analytic hierarchy process (AHP) by implementing them for the preparation of landslide susceptibility maps. Shimla, a district in Himachal Pradesh (H.P.), India was chosen for the study. A landslide inventory containing more than 1500 landslide events was prepared using previous literature, available historical data and a field survey. Out of the total number of landslide events, 30% data was used for training and 70% data was used for testing purpose. The frequency ratio, Shannon’s entropy and AHP models were implemented and three landslide susceptibility maps were prepared for the study area. The final landslide susceptibility maps were validated using a receiver operating characteristic (ROC) curve. The frequency ratio (FR) model yielded the highest accuracy, with 0.925 fitted ROC area, while the accuracy achieved by Shannon’s entropy model was 0.883. Analytic hierarchy process (AHP) yielded the lowest accuracy, with 0.732 fitted ROC area. The results of this study can be used by engineers and planners for better management and mitigation of landslides in the study area.

Landslide Susceptibility Zonation Mapping in and Around Dharamshala, Himachal Pradesh Using Bivariate Statistical Techniques – A Comparative Study

2020 ◽  
Author(s):  
Kumari Sweta ◽  
Ajanta Goswami
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Himachal Pradesh ◽  
Logic Model ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Statistical Techniques

<p><strong>Abstract</strong><strong>:</strong> 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. In this study frequency ratio, information value and fuzzy logic models were used for landslide susceptibility mapping of an area of 356km<sup>2</sup> in and around Dharamshala, Himachal Pradesh, using earth observation data. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. The thrust for infrastructure development has led to a need for prior planning to minimize the consequences of landslide hazards. The final produced landslide susceptibility zonation maps with better accuracy could be used for land-use planning to prevent future losses. A landslide inventory for the study area was prepared through visual interpretation of high-resolution satellite imagery and available inventory report. Remote sensing data and other ancillary data like geological data were collected and processed in the GIS environment to generate thematic maps of parameters influencing landslide occurrence. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. Using these parameters and landslide inventory weight and membership value was calculated for the Frequency ratio, information value and Fuzzy logic model, respectively. In the frequency ratio and information value model, all the landslide causative parameters were arithmetically overlaid using calculated weights for landslide susceptibility mapping. In the fuzzy logic model, different fuzzy operators were applied to the calculated fuzzy membership values. Unlike the normalization process for membership calculation present study used the cosine amplitude method, which will give more reliable results. A total of ten landslide susceptibility maps (LSM) were produced using two models, 9 from fuzzy logic and 1 from frequency ratio. All the results were verified spatially and statistically using landslide locations and ROC curves. Further, the performance and significance of different outputs were compared to select the most suitable LSM for the study area. Among all fuzzy operators, “gamma” with λ = 0.9 showed the best accuracy (84.3%) and operator “and” has the worst accuracy (77.6%). But among all 9 output maps of fuzzy logic except the output of gamma (λ = 0.9) gives satisfactory LSM rest all show the unacceptable result as the maximum number of pixels is either in very low or high susceptible zone. The validation and comparison result exhibited that the fuzzy logic (accuracy=84.3%) is better than the information value (83.46) and the frequency ratio method (accuracy=83.43%).</p><p><strong>Keywords</strong>: Bivariate Statistical Techniques, Information Value, Frequency Ratio, Fuzzy Logic, ROC</p>

Remote Sensing Data Derived Parameters and its Use in Landslide Susceptibility Assessment Using Shannon’s Entropy and GIS

2012 ◽  
Vol 225 ◽  
pp. 486-491 ◽  
Author(s):  
Hamid Reza Pourghasemi ◽  
Biswajeet Pradhan ◽  
Candan Gokceoglu
Keyword(s):  
Landslide Susceptibility ◽  
Area Under Curve ◽  
Shannon’S Entropy ◽  
Aerial Photographs ◽  
Slope Aspect ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Shannon's Entropy ◽  
The Impact

In recent years, the growth of urban populations in hazardous areas has increased the impact of natural disasters in both developed and developing countries. The purpose of the current study is to assess the landslide susceptibility in Kalaleh township of Golestan province, Iran. In this study the Shannon’s entropy approach was applied. A total of 82 landslide locations were identified primarily from aerial photographs and field surveys. Then eighteen landslides conditioning factors were prepared in GIS. These landslide conditioning factors are: slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, soil texture, distance from faults, distance from rivers, distance from roads, fault density, road density, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). Using these conditioning factors, landslide susceptibility index was calculated using Shannon’s entropy. For model validation, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curves for landslide susceptibility maps were drawn and the area under curve values was calculated. Verification results showed 82.15% accuracy. According to the results of the AUC (area under curve) evaluation, the map produced exhibits satisfactory properties.

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