Date of Graduation

5-2018

Document Type

Thesis

Degree Name

Master of Science in Geology (MS)

Degree Level

Graduate

Department

Geosciences

Advisor/Mentor

Mohamed Aly

Committee Member

Gregory Dumond

Second Committee Member

Jason Patton

Keywords

Arkansas, Boston Mountains, Fuzzy Modeling, Landslide Susceptibilty, Regression Analysis, Weather Severity Index

Abstract

The State of Arkansas is prone to numerous geohazards. This thesis is a twofold study of prominent geohazards in Arkansas: the first fold includes a novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains in NW Arkansas, and the second fold is a GIS-based regression modeling of the extreme weather patterns at the state level. Each study fold is presented in this thesis as a separate chapter embracing a published peer-reviewed paper. In the first paper, I have used the analytical hierarchy process to assign preliminary statistical weights to the most cogent variables influencing mass wasting in the central Boston Mountains. These most significant variables are then incorporated in Fuzzy modeling of mass wasting susceptibility within the 1200 km2 study area. For comparison and accuracy assessment, a second model has been established using a conventional weighted overlay (WO) approach. Results indicate that the developed novel approach is superior, with approximately 83% accuracy, to the traditional WO approach that has a marginal success of about 28% accuracy. Road related mass wasting events recorded by the Arkansas Department of Transportation have been used to validate both models. In the second paper, I have conducted a systematically gridded analysis of severe weather events, including tornadoes, derechos, and hail, during 1955-2015. The study examines and statistically determines the most significant explanatory variables contributing to the spatial patterns of severe weather events between 1955 and 2015, consequently it identifies severity indices for the entire state. These weather-related hazards and their associated risk will always abide; therefore, the best defense is employ geospatial technologies to plan for hazard mitigation. The mass wasting model developed in this study contributes pivotal information for identifying zones of high risk along roadways in NW Arkansas, which definitely can be adapted to avoid disastrous road failures. In addition, the weather-related severity indices determined at the state level can profoundly benefit state and federal agencies focused on increasing the availability of public and private storm shelters in previously under-represented zones of high risk. This undoubtedly will save lives from unavoidable catastrophic events across the entire state.

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