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Predicting Urban Stormwater Quality in Data-Deficient Areas: Enhancing Deep Tunnel Systems with Machine Learning Techniques

Author(s): Haibin Yan; David Zhu

Linked Author(s): David Z. Zhu

Keywords: Data-deficient; Deep tunnel; Machine learning; Prediction model; Semi-supervised learning; Urban stormwater quality

Abstract: Deep tunnel systems are designed to mitigate the risk of flooding and corresponding pollution in urban areas. Predictive modeling can enhance the functionality of deep tunnel systems. Machine learning is an effective predicting tool in data-deficient areas. By integrating data from different catchments, the model performance can be enhanced. Consideration of catchment characteristics can improve the model's generalization capacity. The pseudo-labeling learning can elevate the model's predictive ability.


Year: 2024

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