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Research

Highlighted Projects
 

Our research projects are centered around different topics related to Food-Energy-Water nexus (FEW), Integrated Water Resources Management (IWRM), and Coupled Natural-Human systems (CNH). Our goal is to develop new techniques and design policies to improve resources management and performance of infrastructure systems. Our research focuses on:

Flood modeling and management

Integrated Water Resources Modeling and Management

Systems Thinking and Analysis

Water-Energy-Food Nexus

Artificial Intelligence and Hydroinformatics

Multi-objective Optimization and Decision Making in Coupled Human-Natural Systems

Phase I

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Vulnerability of Coastal Stormwater Management Ponds to Non-Stationarity and Compound Effects of Sea Level Rise and Inland Flooding.

Funding Source - South Carolina Sea Grant Consortium

Phase II

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Vulnerability of Coastal Stormwater Management Ponds to Compound Effects of Sea Level Rise and Inland Flooding.

Funding Source - South Carolina Sea Grant Consortium


 

Energy Sustainability
 

parameters This project seeks to study the sustainability of different electricity generation technologies. The focus of the study is on sustainability based on available resources in a region.

Coastal Hazard and Compound Flood risk analysis due to climate change and sea level rise
 

ahad_research The goal of this project is to determine the Socio-environmental coastal vulnerability of South Carolina coast and to simulate compound flood.


 

Optimizing LID Implementation in Charleston, South Carolina: An Agent-BasedModelling Approach
 

placeholder We are working to produce a means of modelling both urban land management options and decisions for the purpose of implementing Low Impact Development strategies in atrisk areas with significant historic value.

Flood early warning system
 

placeholder We are working on different pieces of a smart early warning system using smart computer vision. Additionally, a watershed-scale feasible study, has been proposed.


 

Integrated Water Resources Management and application of AI in hydrologic modeling and predictions
 

ai_hydro_1 The goal of the project is to is to optimize the operation of complicated water systems such as river basin systems and reservoirs. We are also working on the application of Artificial Intelligence in hydrologic modelling as an alternative to conceptual models as well using AI for post processing the long term forecasts of hydrologic models.

Finding the correlation between Socioeconomic variables and the number of COVID-19 cases
 

covid_research We evaluated the correlation between several Socioeconomic data and number of approved COVID-19 cases and tried to predict the number of cases in each state using a machine learning Method. 21 Socio Economic indices were retrieved, 30 States were used to train the Model at each time, and the rest of the states were used to evaluate the model accuracy.