Understanding Florida’s Sinkhole Hazards: Hydrogeological Laboratory Study

Perez, Adam; Nam, BooHyun; Chopra, Manoj; Sallam, Amr

doi:10.1061/9780784480472.053

Cited by 2 publications

(1 citation statement)

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Kim¹,

Nam²

2018

Proceedings of the 15th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst and Th

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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. 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

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Section: Introductionmentioning

confidence: 99%