Contaminant Source Characterization Using Logistic Regression and Local Search Methods
Publication: World Environmental and Water Resources Congress 2008: Ahupua'A
Abstract
Given a set of contaminant concentration observations at sensors in a water distribution network, an inverse problem can be constructed to identify the contaminant source characteristics (including location, strength and release history) by coupling a water distribution simulation model with an optimization method. This approach, however, requires a large number of time-consuming simulation runs to evaluate potential solutions, and it may be difficult to converge on the best solution or set of possible solutions within a reasonable computational time. For this reason, it is desirable to appropriately reduce the decision space over which the optimization procedure must search to reduce the computational burden and to potentially produce faster convergence. We propose a method to reduce the decision space by efficiently identifying the probability of each point or demand node being a contaminant source location using mostly off-line computations. Then, the most likely source locations are used as a good starting point for local search methods to obtain the optimal injection profile(s) to match the observed concentration profile(s) over time. The proposed approach is demonstrated for a contamination source identification problem using an illustrative water distribution network.
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© 2008 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Analysis (by type)
- Engineering fundamentals
- Environmental engineering
- Equipment and machinery
- Freight transportation
- History
- History and Heritage
- Infrastructure
- Logistics
- Models (by type)
- Optimization models
- Pollutants
- Pollution
- Practice and Profession
- Probe instruments
- Regression analysis
- Statistical analysis (by type)
- Transportation engineering
- Water and water resources
- Water management
- Water pollution
- Water supply
- Water supply systems
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