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Sinkholes are one of the most common geohazards occurring in East Central Florida (ECF). Identifying areas prone to sinkholes is vital for land use planning in the ECF area, and thus, sinkhole hazard mapping plays a critical role. The present study presents (1) sinkhole hazard maps of ECF by using frequency ratio (FR) and artificial neural network (ANN) models and (2) a validation and comparison of the performance of two models. An inventory map with a total of 757 sinkhole locations was prepared from Florida subsidence incident reports (FSIR). 70% (530 sinkholes) were randomly selected to calibrate the sinkhole hazard models, and the remaining 30% (227 sinkholes) were used for the model validation. Five sinkhole contributing factors were considered including age of sediment deposition, hydraulic head difference, groundwater recharge rate, overburden thickness, and proximity to karst features. The relationship between sinkhole occurrence and sinkhole contributing factors was investigated through a GIS-based statistical analysis. Introduction Karst topography occurs in terrains that contain distinctive landforms and hydrology created primarily from the dissolution of soluble bedrock. The soluble bedrock is typically carbonate rocks such as limestone, dolomite, and gypsum and common features of karst topography include sinkholes, springs, and caverns, etc. According to the US Geological Survey (USGS), karst topography makes up about 20 percent of the Nation’s land surface and extensive karst topography is found in the sates of Florida, Texas, Alabama, Missouri, Kentucky, Tennessee, and Pennsylvania (Weary and Doctor, 2014). A sinkhole is one of the most common and frequent natural geohazards in the karst terrain. It poses a threat not only to public safety but also to property, resources, and the environment. Sinkholes create at least $300 million in damages each year in the United States and the actual damage is probably much higher than this estimate as there is no formal nationwide tracking of damage costs (Weary, 2015). The Florida Office of Insurance Regulation reports that insurers received a total of 24,671 claims for sinkhole damage in Florida between 2006 and 2010 totaling $1.4 billion. The report shows the insurers’ expense has been rising with an increasing trend in both frequency and severity of sinkholes (FLOIR, 2010). Considering that a number of factors contribute to sinkhole formation in karst areas, geologists and geotechnical engineers are faced with a difficult task when attempting to identify the most important factors that lead to sinkhole development. Past studies on sinkhole formation in Florida show that hydrogeological factors are strongly linked to its occurrence (Wilson and Beck, 1992; Tihansky, 1999; Xiao et al., 2016; Perez et al., 2017). These sinkhole-related factors include hydraulic head difference, groundwater recharge rate, overburden soil thickness, aquitard layer thickness, water table depth, and distance to karst features, as well as geological age (Kim et al., 2018, submitted). Use of geospatial data and geographic information system (GIS) technologies enables sinkhole hazard assessment and mapping. Various methods have been proposed for geohazard mapping, and can be generally classified into two groups: qualitative methods and quantitative methods. The qualitative methods include field investigation procedures and/or overlaying of different factor maps and applying predetermined weights to develop a hazard map. The quantitative methods contain artificial intelligence techniques such as artificial neural networks (ANN) and fuzzy systems, statistical techniques and deterministic or probabilistic procedures (Bhardwaj and Venkatachalam, 2014). This study aims to produce sinkhole hazard maps of East Central Florida (ECF), using frequency ratio (FR) and artificial neural network (ANN) methods using GIS as a YongJe Kim Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA, yongkim@knights.ucf.edu Boo Hyun Nam Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA, boohyun.nam@ucf.edu (corresponding)

A Comparative Study of Karst Sinkhole Hazard Mapping Using Frequency Ratio and Artificial Neural Network for East Central Florida