Stormwater Symposium

Poster

393447 - On the challenges of monitoring green water infrastructure with real time sensors

Wednesday, June 6
10:30 AM - 12:00 PM
Location: Mirage Room
Co-Authors: Hashim Alyousef, Corvallis, OR – Oregon State University

Over the last ten years, a large amount of research has been conducted on design and implementation of urban Best Management Practices (BMPs), and on the various effects they have on reducing runoff and improving water quality. However, very little research has focused on on-the-ground challenges with deploying a large number of sensors for long-term monitoring of BMPs in the field. Real time monitoring of BMPs can be immensely useful in tracking the overall effectiveness of a network of BMPs in an urban landscape, as well as identifying maintenance needs. On the other hand, employing real-time sensors can be expensive as well as challenging to manage. Errors in data collected by real-time sensors, such as flow and water level sensors, can occur frequently due to multiple reasons. Some of these reasons include sensor drifts over time, air temperature fluctuations, and voltage fluctuations in battery connected to data loggers. These errors can lead to significant underestimation or overestimation of hydrologic processes in the BMPs, as well as inaccurate assessment of BMP performance.
The goal of this research is to identify sources and types of errors that arise in real-time sensors deployed in urban BMPs, and develop a novel adaptive method for real-time error correction. Error analysis was conducted using real-time data on flows, weather, and other hydrologic parameters, collected at a field research site on green infrastructure. In this presentation, we will present our findings on this research, including effectiveness of the proposed adaptive error correction method.

Meghna Babbar-Sebens

Associate Professor
Oregon State University

Dr. Meghna Babbar-Sebens conducts interdisciplinary, computational research in the field of Hydroinformatics to develop innovative and effective solutions for sustainable planning and management of water-based systems. Her research investigates innovations in a wide variety of Hydroinformatics approaches, including computational modeling of complex water-based systems, multi-objective optimization, interactive optimization, noisy optimization, evolutionary computing, multi-agent models, Markov decision processes, neural networks, human-computer interaction, data assimilation, high performance computing, etc. These innovations help solve a variety of problems, such as:
> How can communities collaborate via web-based technologies to plan and design conservation practices or green stormwater practices on their landscape?
> How can high performance computing and optimization algorithms be used to design short term and long term watershed adaptation alternatives, for communities combatting flooding, droughts, and/or water quality impacts due to changing climate and anthropogenic drivers?
> How can observations from different types of sensors (e.g., in-situ instruments, satellites, and unmanned aerial systems (UASs), etc.) be used to improve data assimilation in water quality models?
> What types of data-driven, machine learning models are useful for simulating complex water systems when we don't know the exact mechanistic process in the system?

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