scholarly journals Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method

2015 ◽  
Vol 29 (4) ◽  
pp. 1149-1165 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Biswajeet Pradhan ◽  
Mustafa Neamah Jebur
Keyword(s):  
Support Vector Machine ◽  
Frequency Ratio ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Analysis ◽  
Flood Susceptibility ◽  
Frequency Ratio Method

scholarly journals FRMOD, a Python tool for statistical landslide susceptibility assessment

2021 ◽  
Vol 3 ◽  
pp. 1-6
Author(s):  
Dávid Gerzsenyi
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Predictive Performance ◽  
Ratio Method ◽  
Analysis Method ◽  
Frequency Distributions ◽  
Landslide Occurrence ◽  
Susceptibility Analysis ◽  
Gis Tools ◽  
Frequency Ratio Method

Abstract. Locating landslide-prone slopes is important, as landslides often threaten life or property where they occur. There is an abundance of statistical methods in the literature for estimating susceptibility to landslides, i.e., the likelihood of landslide occurrence based on the analyzed conditions. Still, there is a lack of readily available GIS tools for landslide susceptibility analysis, making it hard to reproduce or compare the results of different susceptibility assessments. The FRMOD is a Python-based tool for conducting landslide susceptibility analysis with the frequency ratio method. The frequency ratio method yields susceptibility estimates by comparing the frequency distributions of a set of variables from the sample landslide areas to the distributions for the whole study area. The estimates show the level of similarity to the sample landslides. The two main inputs of the tool are the raster grids of the analyzed continuous (e.g., elevation, slope) and thematic (e.g., lithology) variables and the mask grid that marks the landslide and the non-landslide areas. The analysis is performed with cross-validation to measure the predictive performance of the model. Data computed during the analysis is stored along the final susceptibility estimates and the supplementary statistics. The script reads and writes GDAL-compatible rasters, while the statistics can be saved as text files. Basic plotting functionalities for the grids and the statistics are also built-in to quicken the evaluation of the results. FRMOD enables the swift testing of different analysis setups and to apply the same analysis method for different areas with relative ease.

Flood Susceptibility Analysis in Chennai Corporation Using Frequency Ratio Model

2021 ◽  
Author(s):  
Logesh Natarajan ◽  
Tune Usha ◽  
Muthusankar Gowrappan ◽  
Bavinaya Palpanabhan Kasthuri ◽  
Prabhakaran Moorthy ◽  
...  
Keyword(s):  
Frequency Ratio ◽  
Ratio Model ◽  
Susceptibility Analysis ◽  
Flood Susceptibility ◽  
Frequency Ratio Model

scholarly journals A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4170 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
Fang Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Square ◽  
Ratio Method ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Diagnosis Model ◽  
Transformer Fault

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

scholarly journals A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7653 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Lalit Kumar ◽  
Farzin Shabani
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Technique ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
The Individual

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

scholarly journals Frequency ratio method for seismic modelling ofγDoradus stars

2005 ◽  
Vol 443 (1) ◽  
pp. 271-282 ◽  
Author(s):  
J. C. Suárez ◽  
A. Moya ◽  
S. Martín-Ruíz ◽  
P. J. Amado ◽  
A. Grigahcène ◽  
...  
Keyword(s):  
Frequency Ratio ◽  
Ratio Method ◽  
Seismic Modelling ◽  
Frequency Ratio Method
Sign in / Sign up

Export Citation Format

Share Document