Abstract

Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

Highlights

  • Techniques to predict a response variable given a set of characteristics are required in several scientific regularities

  • Mineral potential mapping is aided by bivariate statistical analysis (BSA) techniques

  • Carranza (2004) used weight of evidence (WoE) modelling to map the mineral potential in the administrative province of Abra in northwestern Philippines. Their achievements indicate the plausibility of WoE in the mineral potential mapping of large areas with a small number of mineral prospects

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Summary

Introduction

Techniques to predict a response variable given a set of characteristics are required in several scientific regularities. Different types of bivariate statistical analysis (BSA) have been established, for example, frequency ratio (FR), weight of evidence (WoE), and evidential belief function (EBF) (Yalcin, 2008). Each of these methods requires specific mechanisms for calculation, all of these methods operate by using the same concept. Carranza (2004) used WoE modelling to map the mineral potential in the administrative province of Abra in northwestern Philippines. Researchers have applied WoE in mapping mineral potential (Bonham-Carter et al, 1989) and it remains popular in this area of research (Carranza et al, 2008)

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