Browsing by Author "Alipour, Atieh"

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    Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States
    (Nature Portfolio, 2023) Yarveysi, Farnaz; Alipour, Atieh; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid; University of Alabama Tuscaloosa
    The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making.
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    From Hazard to Impact: Smart Flood Forecast System and Damage Prediction
    (University of Alabama Libraries, 2022) Alipour, Atieh; Moradkhani, Hamid; University of Alabama Tuscaloosa
    Floods are among the most devastating natural hazards across the globe, and it isexpected to be escalated in the future mostly due to the warming climate. Depside tremendousnational governments' protection efforts, they still result in the loss of lives and properties.Understanding flood dynamics and developing fast and reliable flood forecasting systems areessential for mitigating the associated risks and implementing proactive risk managementstrategies. This dissertation is an attempt to characterize flood hazard and enhance flood riskassessment by leveraging enhanced statistical approaches and recent physical models. The firstfour chapters present previous peer-reviewed studies and are as follows:The first chapter is on assessing flash flood characteristics including frequency, duration,and intensity in addition to their associated property damages. The second chapter presents asystematic framework that considers a variety of features explaining different components ofrisk and examines multiple machine learning methods to predict flash flood damage. In chapterthree, we assess the sensitivity of the HEC-RAS 2D to its configuration factors and parameters.In chapter four, we are developing a new tropical cyclone scaling system that uses Copulas forcategorizing Tropical Cyclones (TCs) based on the likelihood of a given set of severity forrainfall, surge, and wind speed. Finally, in chapter five, we present a systematic framework thatuses Deep Learning (DL) algorithms, and hydrodynamic models to generate probabilistic floodwater levels at different locations along coastal rivers.