Browsing by Author "Fazil, A. Z"

Now showing 1 - 2 of 2

Embargo
Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
(Elsevier Ltd, 2024-09-15) Fazil, A. Z; Gomes, P. I.A.; Sandamal, R.M. K
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.
Embargo
Predicting microplastic transport in open channels with different bed types and river regulation with machine learning techniques
(Elsevier Ltd, 2025-07-31) Fazil, A. Z; Dhawala Wijeratna D.D.S.; Gomes. P. I. A
Microplastic transport in open channels with varying bed (acrylic, medium sand, and cobble) and regulation (weirs and sluice gates) configurations for three types of microplastics (densities: 910–1350 kg/m3 ) with varying discharges were studied in a laboratory setup. In general, bivariate correlations between retention and independent variables (e.g., discharge) were weak. Also, retention was dependent on the type of river regulation. The sluice gate retained statistically fewer microplastics compared to the weir, excluding the influence of bed conditions. Nevertheless, it permitted the escape of two microplastic types to the downstream. These complex observations were well predicted in machine learning models and validation using a gravel bed confirmed model robustness. Decision Tree performed best among individual models, with a co-efficient of correlation (R2 ) of 0.99 and mean square error (MSE) of 2.00. The stacking ensemble model combining Decision Tree, Random Forest, and XGB achieved a similar accuracy (R2 of 0.99, MSE of 2.00) while reducing the likelihood of overfitting. These results clearly demonstrate the strength of stacking in enhancing both predictive performance and generalization capability. SHapley Additive exPlanations (SHAP) analysis, including dependency plots highlighted density and bed roughness as key factors of retention. The developed ML model offers a resilient predictive tool for understanding microplastic transport, suggesting ways to address pollution through river management.