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

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.

The Comparison Between Certainty Factor Method and Index of Entropy Method For Landslide Hazard Assessment: South Western Chamba District, Himachal Pradesh, India

The present study reflects the contributions of geo-environmental factors that were analyzed for the development of landslide hazard zonation map using certainty factor method and index of entropy method. Heavy rainfall, unscientific excavation of slopes during road construction, expansion of infrastructure, and unplanned growth in urban population were the major factors for unstable slopes in the Lesser Himalayan region. Historical database, interpretation of satellite and Google earth images were used to identification of 248 landslides. The data collected using remote sensing images have been verified by conducting ground truth surveys undertaken from January 2018 to October 2020 in preparing the landslide inventory of the study area. Inventory thus generated was divided into 70% training and 30% validation datasets. Relationships between slope failure and its causative factors (relief, slope, aspect, curvature, lithology, soil, weathering, land use, lineament density, rainfall, and density of drainage networks) were analyzed by using certainty factor (CF) and index of entropy (IOE) methods. The analysis of all causative factors and assigning relative weightage values by using the index of entropy and certainty factor models leads to the generation of Landslide hazard zonation maps of the region. Finally, the landslide prediction accuracy of hazard zonation maps was calculated by drawing Successive Rate Curve (SRC) curves for both training and validation datasets. The outcomes of this study will be useful to government agencies, planners, decision-makers, researchers, and general land-use planners for sustainable development of the study area.

Similarity Comparison of Multiple Coronavirus Sequences from 2D to 1D Linearizing Transformation

Many studies on COVID-19 have been carried out, and it is interesting to apply methods and models to process the whole sequence of RNA. Similarity comparison of SARS-CoV-2 genomes plays a key role in naturally tracing its ori-gin in scientific exploration, and further explorations are required. In this paper, an innovative of transformation from a 2D density matrix to 1D measuring vector is proposed based on the A5 module of the MAS for visualization. The core transformation projects whole RNA sequences of multiple coronaviruses in 2D matrices and then forms 1D measuring vectors on variant maps. The relationships of SARS-CoV-2 genomes are compared by their similarity properties and genomic index of entropy quantities applied to classify relevant results into groups.

Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan

The landslide is one of the natural disasters which claim human lives and incur huge economic losses, especially in the mountainous area. The main aim of this study is to develop different zones of landslide-prone area using the index of entropy (IOE) at the Ossey watershed area in Bhutan. During the landslide inventory, 164 landslides were identified of which 115 locations were used for the training dataset while the remaining 49 locations were used for the validation dataset. A total of ten causal factors were used for this study including elevation, slope, aspect, slope curvature, stream power index, normalized difference vegetation index (NDVI), distance from the road, distance from the river, lithology, and rainfall. The IOE was used to obtain the relationship between the landslide events and the causal factors. The most influential causal factors were NDVI, slope, and rainfall with the weightage of 0.377, 0.347, and 0.175 respectively as per the IOE. The final landslide susceptibility map was classified into five classes using the geometrical interval classification. The validation was done using the receiver operating characteristic (ROC) curves and the kappa index. The area under the curve (AUC) for the success rate and prediction rate was 0.7821 and 0.8377, respectively. The kappa index using the training dataset and validation dataset were 0.4111 and 0.4898, respectively. The final landslide susceptibility map is accurate enough for the future references by the decision-makers and the engineers.

Similarity Comparison of Multiple Coronavirus Sequences from 2D to 1D Linearizing Transformation

Many studies on COVID-19 have been carried out, and it is interesting to apply methods and models to process the whole sequence of RNA. Similarity comparison of SARS-CoV-2 genomes plays a key role in naturally tracing its origin in scientific exploration, and further explorations are required. In this paper, an innovative of transformation from a 2D density matrix to 1D measuring vector is proposed based on the A5 module of the MAS for visualization. The core transformation projects whole RNA sequences of multiple coronaviruses in 2D matrices and then forms 1D measuring vectors on variant maps. The relationships of SARSCoV-2 genomes are compared by their similarity properties and genomic index of entropy quantities applied to classify relevant results into groups.