Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3748
Title: Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
Authors: Fazil, A. Z
Gomes, P. I.A.
Sandamal, R.M. K
Keywords: Explainable artificial intelligence
Microplastic density
Open channel discharge
Vegetation
Issue Date: 15-Sep-2024
Publisher: Elsevier Ltd
Citation: V. Menan, A. Gawesha, P. Samarasinghe and D. Kasthurirathna, "DS-HPE: Deep Set for Head Pose Estimation," 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2023, pp. 1179-1184, doi: 10.1109/CCWC57344.2023.10099159.
Series/Report no.: Environmental Pollution;Volume 357
Abstract: This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.
URI: https://rda.sliit.lk/handle/123456789/3748
ISSN: 02697491
Appears in Collections:Department of Civil Engineering

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