AI-powered Diagnosis Augmented by Self-sustaining Sensing System for Intelligent Wastewater Infrastructure Management

Project Goals

The proposed work will revolutionize the urban wastewater system by transforming hydrological and hydraulic science and expert knowledge into customized deep models, augmented by self-sustaining sensing system for intelligent wastewater management.

Research Challenges

We propose (1) training a wastewater hydraulic surrogate model using Graph Neural Network (GNN); (2) augmenting the surrogate model with a physical monitoring system deployed on the wastewater infrastructure with sensors; and (3) automatic anomalies detection, allowing timely corrective actions.

Current/Final Results (summary)

In progress.

Publications

1. Jiang, C., Wang, W., Li, J., Pan, N., & Ku, W. S. (2024). Deep Spatio-Temporal Encoding: Achieving Higher Accuracy by Aligning with External Real-World Data, DARLI-AP@EDBT 2024.

Presentations and images/Videos demonstrating the project

1. “AI-powered Urban Water System: Modeling Spatio-temporal Dependencies with Graph Neural Networks” The 2024 Symposium for Student Innovation, Research, and Creative Activities (SSIRCA)

Data, Demos and Software Downloads (with documentation)

1. Data

Patents

None

Other relevant information

None

Broader Impacts

The proposed research will have a positive impact on public safety and health in coastal and other communities with degraded wastewater systems. As early access to advanced AI decision-making tools has the potential to exacerbate existing inequalities by giving more resources to those already with access to new technologies. The proposed research seeks to counteract this by putting AI research into practice and ensuring that AI benefits all residents equally through an AI-augmented wastewater system.

Educational material (with documentation)

1. COSC 6338 Machine Learning
2. COSC 6354 Artificial Intelligence

Acknowledgement

Gratitude is extended to Corpus Christi Water for their invaluable collaboration in supporting this work.

Disclaimer

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Award number

2318641

Duration

October 1, 2023 to September 30, 2026

PI, co-PI(s)

Wenlu Wang

Hua Zhang

Chen Pan

Student(s)

Qiming Guo

Collaborators, etc.

1. Corpus Christi Water (CCW)

2. Water & Environmental Systems Analysis (WESA) Lab

3. AIoE Lab

Point of Contact

Wenlu Wang (wenlu.wang@tamucc.edu)

Date of Last Update

10/15/2023