Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated...

Microplastic pollution in coastal environments is an escalating global concern, yet its spatial distribution and accumulation dynamics remain inadequately addressed. This study pioneers the use of an integrated Geographical Information Systems (GIS) and Artificial Neural Network (ANN) model to predict microplastic accumulation zones along Kelantan's coast, Malaysia. Leveraging key environmental variables including shoreline erosion, tidal influence, and sediment transport which the model demonstrates high predictive accuracy (R² = 0.972), identifying erosion-prone areas as significant deposition zones. The strong correlation between tidal dynamics and microplastic abundance highlights the influence of hydrodynamic forces on pollution patterns. As one of the first applications of ANN modeling in Malaysian coastal environments, this research offers a scalable, data-driven tool for coastal managers to optimize pollution control strategies. These findings provide critical insights for targeted marine pollution interventions, contributing to ongoing global efforts to protect vulnerable coastal ecosystems.