Diagnosis Of The Artificial Intelligence-Based Predictions Of Flow Regime In A Constructed Wetland For Stormwater Pollution Control

Keywords

Acoustic Doppler Velocimeter; Artificial neural network; Constructed wetland; Genetic programming; Stormwater Management; Velocity Flow Field

Abstract

Monitoring the velocity field and stage variations in heterogeneous aquatic environments, such as constructed wetlands, is critical for understanding hydrodynamic patterns, nutrient removal capacity, and hydrographic impact on the wetland ecosystem. Obtaining low velocity measurements representative of the entire wetland system may be challenging, expensive, and even infeasible in some cases. Data-driven modeling techniques in the computational intelligence regime may provide fast predictions of the velocity field based on a handful of local measurements. They can be a convenient tool to visualize the general spatial and temporal distribution of flow magnitude and direction with reasonable accurancy in case regular hydraulic models suffer from insufficient baseline information and longer run time. In this paper, a comparison between two types of bio-inspired computational intelligence models including genetic programming (GP) and artificial neural network (ANN) models was implemented to estimate the velocity field within a constructed wetland (i.e., the Stormwater Treatment Area in South Florida) in the Everglades, Florida. Two different ANN-based models, including back propagation algorithm and extreme learning machine, were used. Model calibration and validation were driven by data collected from a local sensor network of Acoustic Doppler Velocimeters (ADVs) and weather stations. In general, the two ANN-based models outperformed the GP model in terms of several indices. Findings may improve the design and operation strategies for similar wetland systems.

Publication Date

7-1-2015

Publication Title

Ecological Informatics

Volume

28

Number of Pages

42-60

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.ecoinf.2015.05.001

Socpus ID

84934966672 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84934966672

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