A5030260337- Flood Risk Assessment and Management
- Hydrology and Watershed Management Studies
- Scientific Computing and Data Management
- Environmental Monitoring and Data Management
- Hydrological Forecasting Using AI
- Tropical and Extratropical Cyclones Research
- Urban Stormwater Management Solutions
- Distributed and Parallel Computing Systems
- Research Data Management Practices
- Meteorological Phenomena and Simulations
- Advanced Computational Techniques and Applications
- Soil and Water Nutrient Dynamics
- Hydrology and Drought Analysis
- Coastal and Marine Dynamics
- Precipitation Measurement and Analysis
- Simulation Techniques and Applications
- Water resources management and optimization
- Evacuation and Crowd Dynamics
- Traffic Prediction and Management Techniques
- Water Quality Monitoring Technologies
- Soil erosion and sediment transport
- Geographic Information Systems Studies
- Water Systems and Optimization
- Groundwater and Isotope Geochemistry
- Service-Oriented Architecture and Web Services
University of Virginia
2016-2025
Engineering Systems (United States)
2019-2024
Charles River Laboratories (Netherlands)
2024
ORCID
2021-2024
McCormick (United States)
2014-2023
John Wiley & Sons (United States)
2018-2020
AstraZeneca (United Kingdom)
2017
University of South Carolina
2007-2016
University of Washington
2014
United States Department of the Interior
2009
Abstract Geoscientists now live in a world rich with digital data and methods, their computational research cannot be fully captured traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to document, share, cite all products including data, software, provenance. This article proposes best practices for GPF authors make methods openly accessible, citable, well documented. publication objects empowers scientists manage as valuable scientific assets open...
Many coastal cities are facing frequent flooding from storm events that made worse by sea level rise and climate change. The groundwater table in these low relief is an important, but often overlooked, factor the recurrent locations face. Infiltration of stormwater water intrusion due to tidal forcing can cause already shallow tables quickly toward land surface. This decreases available storage which increases runoff, system loads, flooding. Groundwater forecasts, could help inform modeling...
Abstract Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow models are used to simulate pluvial flooding. Because these require significant computational resources and have long run times, they often unsuitable real‐time at a street scale. This study explores potential of machine learning method, Random Forest (RF), serve as surrogate model...
Abstract The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making more accessible, but are, most cases, limited to particular or classes also lack type collaborative iterative functionality needed enable shared collection modeling workflows. File sharing systems currently many scientific communities for private preliminary intermediate products do not support capture, description, visualization, annotation. In this...
Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With development Service Oriented Architecture (SOA) web technologies, it possible conduct open, extensible integrated across network in which resources can be accessed integrated, further distributed simulations performed. This open web-distributed approach likely enhance use existing attract diverse participants. this approach, participants from different physical locations...
Flooding increases in recent years, particular for coastal communities facing sea level rise, have brought renewed attention to real-time, street-scale flood forecasting. Such models using conventional physics-based modeling approaches are often unrealistic real-time decision support use cases due their long model runtime. Machine learning offers an alternative strategy whereby a surrogate can be trained mimic relationships present within the and, after training, run seconds rather than...
Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of can improve performance, creating policies for smart an active area study. This research explores reinforcement learning (RL) create mitigate flood risk. RL trained using a model hypothetical urban catchments with tidal boundary two retention ponds controllable valves. RL's performance compared...