Particle Swarm Optimization for Inverse Modeling of Soils in Urban Green Stormwater Infrastructure Sites
Publication: Journal of Sustainable Water in the Built Environment
Volume 10, Issue 2
Abstract
The measurement of soil parameters at green stormwater infrastructure (GSI) sites is a labor and time-intensive process. Use of machine learning and inverse modeling techniques to estimate soil parameters provides an answer to this issue. In this paper a particle swarm optimization (PSO) algorithm is used in conjunction with inverse modeling using Hydrus-1D to estimate soil parameters. The novelty of this work is the implementation of PSO to identify soil infiltration models in a functioning urban field site using data from deployed sensors. The linear bioinfiltration site, located in Philadelphia, Pennsylvania, has two layers of soil: a top layer designed for the site and a lower layer native to the site. The PSO was used to estimate parameters for each of these two soils, as well as the depth of the top engineered soil. The resulting simulation using the estimated parameters showed a promising fit to measured soil moisture data, an RMS error of 0.017 in validation testing, and the parameters themselves were estimated more accurately than assuming a standard soil type. This lays the groundwork for using PSO and inverse modeling in conjunction with continuous soil moisture monitoring to enable long-term continuous modeling of GSI sites to determine performance degradation and enable on-demand maintenance.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the Pennsylvania Department of Transportation (PennDOT) for their support and funding. The opinions presented in this publication are those of the authors and do not necessarily express the opinions of the PennDOT. Reference in this report to any commercial product, process, or service, or the use of any trade, firm, or corporation name is for general informational purposes only and does not constitute an endorsement or certification of any kind by the authors. This project is a joint research initiative of the Villanova Center for Nonlinear Dynamics and Control and the Villanova Center for Resilient Water Systems.
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Information & Authors
Information
Published In
Journal of Sustainable Water in the Built Environment
Volume 10 • Issue 2 • May 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 8, 2023
Accepted: Nov 12, 2023
Published online: Mar 15, 2024
Published in print: May 1, 2024
Discussion open until: Aug 15, 2024
ASCE Technical Topics:
- Business management
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Geomechanics
- Geotechnical engineering
- Infrastructure
- Materials engineering
- Mathematics
- Models (by type)
- Optimization models
- Parameters (statistics)
- Particles
- Practice and Profession
- Soil mechanics
- Soil properties
- Soil water
- Statistics
- Stormwater management
- Sustainable development
- Urban and regional development
- Urban areas
- Water treatment
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