scholarly journals Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Mahyat Shafapour Tehrany ◽  
Haluk Özener ◽  
Bahareh Kalantar ◽  
Naonori Ueda ◽  
Mohammad Reza Habibi ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Belief Function ◽  
Ensemble Modeling ◽  
Evidential Belief Function ◽  
Gis Applications ◽  
Ensemble Techniques ◽  
Index Of Entropy ◽  
Wildfire Susceptibility ◽  
Ensemble Algorithms ◽  
Better Than

The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

scholarly journals Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors

2018 ◽  
Vol 20 (6) ◽  
pp. 1436-1451 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Ratio ◽  
Groundwater Resources ◽  
Specific Capacity ◽  
Belief Function ◽  
Neural Network Models ◽  
Evidential Belief Function ◽  
Artificial Neural ◽  
Productivity Potential

Abstract This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

scholarly journals A comparative assessment of fuzzy logic and evidential belief function models for mapping artesian zone boundary in an arid region, Iraq

2017 ◽  
Vol 20 (2) ◽  
pp. 497-519 ◽  
Author(s):  
Alaa M. Al-Abadi ◽  
Suaad A. Al-Bhadili ◽  
Maitham A. Al-Ghanimy
Keyword(s):  
Fuzzy Logic ◽  
Arid Region ◽  
Prediction Accuracy ◽  
Assessment Tool ◽  
Belief Function ◽  
Data Availability ◽  
Zone Boundary ◽  
Inference System ◽  
Conditioning Factors ◽  
Evidential Belief Function

Abstract This paper discusses and compares the potential application of the evidential belief function model and fuzzy logic inference system technique for spatial delineation of a groundwater artesian zone boundary in an arid region of central Iraq. First, a flowing well inventory of a total of 93 perennial flowing wells was constructed and randomly partitioned into two data sets: 70% (65 wells) for training and 30% (28 wells) for validation. Twelve groundwater conditioning factors were considered in the geospatial analysis depending on data availability and literature review. The random forest (RF) algorithm was first applied to investigate the most important conditioning factors in groundwater potential analysis. The most important factors with training flowing wells were used to develop predictive models. The prediction accuracy of the developed models was checked using the area under the relative operating characteristic curve. Results showed that the best model with a higher prediction accuracy of 86% was a fuzzy AND model followed by the evidential model with 84%. The main conclusion of this study is that the integrated use of the adapted models with RF offer a rapid assessment tool in groundwater exploration and can be helpful in groundwater management.

Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

2014 ◽  
Vol 73 (2) ◽  
pp. 1019-1042 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Mohammed Hasan Abokharima ◽  
Mustafa Neamah Jebur ◽  
Mahyat Shafapour Tehrany
Keyword(s):  
Land Subsidence ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Function Model ◽  
Evidential Belief Function

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.

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