Current Research Projects
AI-powered Diagnosis Augmented by Self-sustaining Sensing System for Intelligent Wastewater Infrastructure Management
NSF, $599,826, 2023-2026 (Co-PI)
The infiltration and inflow problem can lead to sewer overflows and have serious consequences for public health and the environment. There is a lack of efficient and cost-effective methods for the modeling of infiltration and inflow and automatic anomaly detection and prediction. The proposed research project bridges the gap between AI research and physical sensing systems, enabling putting AI into practice. The nature of an urban wastewater system, characterized by its temporal, spatial, and topological properties, can be represented as a graph with manholes represented as nodes and pipes as edges. Graph Neural Networks (GNN) are particularly well-suited for modeling graph relations with node embeddings preserving node features and edges carrying flow messages pass to each node. The GNN surrogate model will be augmented by an in-situ water pressure monitoring system that leverages emerging energy-harvesting technology for sustainable in-situ monitoring. Mapping the urban wastewater system into a GNN surrogate model, augmented with a physical sensing system, enables infiltration and inflow anomaly detection, predictions of cascading impacts with the GNN backbone, and taking corrective actions before there is harm to public health and safety or the environment.
Smart and Self-Sustaining Early Warning Systems for Coastal Flooding
TGLO, $99,819, 2023-2025 (Co-PI)
This project will develop intelligent sensors conducting sensing, in-situ risk evaluation, and real-time prewarning sustainably for coastal communities to make thorough preventative measures. This project will develop a smart and self-sustaining early-warning system for coastal flooding with state-of-the-art low-power Tiny Machine Learning (TinyML) and energy harvesting solutions. In particular, we will embed sensors, Tiny ML, and connectivity into energy-harvesting-powered IoT devices for sustainable, accurate, and real-time monitoring, prediction, and pre-warning of flooding paths and risk levels. The outcomes of this project would bring great social-and-economic benefits to various communities. The techniques outcomes will be intellectually compelling and appealing to students as they bring together theoretical and engineering concepts.
Where the Runoff Begins: Rethinking the Role of Impervious Area in Urban Stormwater Management
NSF, $199,966, 2021-2024 (PI)
The description of watershed imperviousness has relied on the concepts of total impervious area and effective impervious area. Both are lumped and static measures that emphasize the extent of imperviousness, but they lack consideration of the spatial pattern of various impervious surfaces. This knowledge gap affects the reliability of hydrological models that tend to conceptualize pervious and impervious surfaces as two lumped components with no interactions. This project aims to answer: 1) How do spatial heterogeneity and hydrological connectivity of impervious surfaces affect stormwater runoff in small urban watersheds? 2) How can the understanding of this mechanism benefit the modeling of stormwater runoff? This project conceptualizes the impervious source area of stormwater runoff as a combination of a constant component and a dynamic component with its hydrological connectivity affected by pervious surfaces. Using Corpus Christi in south Texas as an urban laboratory, the project efforts consist of three phases: (i) UAS-aided data collection, (ii) characterization of hydrological connectivity, and (iii) development of a new multi-component curve number model. The findings of this project will lead to a spatially explicit understanding of the effects of the impervious area on stormwater runoff processes and provide an improved scientific basis for developing reliable stormwater management strategies.
Toward Environmentally and Socially Equitable Stormwater Management Fees
NASA, $149,904, 2022-2024 (PI)
To address water quality and environmental issues, a growing number of communities across the United States have levied a separate utility fee particularly for the impact of surface water runoff to provide a dedicated funding source for local stormwater management. There is no consistent guideline on how impervious areas are estimated for the collection of stormwater fees. Existing methods either lack spatial heterogeneity or are prohibitively costly, and there is little attention to satellite data even though they are increasingly accessible with improved spatial resolution. This project is to explore the technical and economic feasibility to design or modify a local stormwater utility fee (SUF) program from satellite data. It brings together a cross-disciplinary team to integrate Earth science, geospatial, and socioeconomic information to advance analyses in satellite-derived impervious area assessment in the context of environmental and social equity. This community-based feasibility study begins with the development of a cost-effective method for estimating impervious coverage of individual land parcels, followed by equity assessment and community decision support for representative neighborhoods. Analyses are drawn from comparisons of individual land parcels and different neighborhoods in Corpus Christi, a mid-sized city in Texas. Beyond engaging local community stakeholders, the methodology and findings bear broad implications for the design of more equitable stormwater fee programs across communities nationwide.
CREST Center for Geospatial and Environmental Informatics, Modeling and Simulations
NSF, $4,995,354, 2021-2026 (Co-PI, Lead of Subproject 2)
The Centers of Research Excellence in Science and Technology (CREST) program supports the enhancement of research capabilities of minority-serving institutions through the establishment of centers that effectively integrate education and research. This center articulates three research subprojects addressing the challenges of coastal resilience by developing new approaches that integrate remote and autonomous sensing with geospatial computing, artificial intelligence/deep learning, and big data analytics for comprehensive coastal zone monitoring at different scales. The focus is on improving resiliency to extreme coastal hazards and episodic and persistent events (e.g., hurricane and sea-level rise). Subproject 1 develops new approaches integrating remote and autonomous sensing with geospatial computing and artificial intelligence to improve coastal zone monitoring and resiliency decision-making. Subproject 2 contributes to the understanding of the urban water cycle and the resilience of water infrastructure through integrated characterization, simulation, and assessments. Subproject 3 investigates how emerging data sources and advanced geospatial computing can be applied to evaluate, assist, and improve a coastal community’s physical, behavioral, and social health after disasters.
MRI: Acquisition of a Leading-edge Portable Geoprobe System with Subsurface Sampling, Logging and Imaging capabilities for Geoscience Research and Education
NSF, $330,818, 2021-2023 (Co-PI)
This leading-edge Geoprobe 7822DT system includes direct-push (DP) drilling capabilities for well/piezometer installation and coring, discrete aseptic water and sediment sampling, and DP mapping instrumentation for formation permeability, lithology, and water content. This system will support: an understanding of the detailed microstratigraphy that controls flow and solute migration; the provision of samples from which accurate physical, microbial and chemical properties can be determined; an in situ determination of contaminant mass distributions and partitioning, particularly within highly heterogeneous substrates and enabling unambiguous and precise characterization of the water storage and pore structure characteristics of any earth formations. These high-resolution depth profile physical characterizations will lead to advances in the scientific understanding of mobilization and transport of contaminants from soils to groundwater to surface water and the long-term chemical cycling within the groundwater-surface water/seawater transition zone.
Data Collection for Oyster Mariculture Interactions with Seagrass
TGLO, $102,978, 2021-2023 (Co-PI)
Texas Cultivated Oyster Mariculture (COM) may impact seagrasses (SG). Oysters and SG commonly co-occur in nature, but few SG studies exist for suspended cage culture. A critical issue is the buffer distance needed to protect SG beds from COM sedimentation. Suspended sediments reduce light to SG, but the relationships between COM operations, suspended sediments and SG are not known. COM siting is based on the untested supposition a 200 ft buffer protects SG. This project is assessing sediment plumes generated by COM, and the effects of different plume characteristics on SG, for data to inform coastal managers in siting decisions. Innovative tools including UAS are applied to characterize sediment trajectories and persistence and quantify impacts to SG. This knowledge will enable resource managers and stakeholders to develop guidelines that protect SG resources and sustain oyster harvest.
SAI: Strengthening Coastal Bend Infrastructure through the Improvement of Science Communication
NSF (#2110817), $49,685, 2021-2023 (Co-PI)
The vulnerability of Texas infrastructure was dramatically highlighted by the February 2021 winter storms which brought down the Texas power grid and access to potable drinking water. It is critical to ensure effective two-way communication between researchers and Coastal Bend communities and decision-making entities. To achieve that goal, the project enables training in science communication for infrastructure-oriented researchers in a highly diverse region of Texas. That effort culminates in a conference that exposes those researchers to their infrastructure-focused counterparts from the social, behavioral and economic (SBE) fields. Using evidence-informed communicative frameworks, this conference fosters convergence in areas of the built and soft infrastructure of coastal regions by bringing SBE scientists alongside natural and physical scientists and engineers. The common foci (such as flooding and groundwater quality, or hurricanes and extreme weather) integrate human- and-social-centered approaches to the infrastructure by concentrating on key issues, theories, and methodologies from SBE perspectives.