Development of a ConvLSTM-Net Model for Coastal Erosion Hazard Prediction Based on Spatiotemporal Data
Received: 4 February 2026 | Revised: 15 March 2026 and 22 March 2026 | Accepted: 23 March 2026 | Online: 3 March 2026
Corresponding author: Willy Boen
Abstract
Coastal erosion is a complex and dynamic process influenced by nonlinear interactions between spatial factors, such as shoreline geometry and land use, and temporal drivers including wave activity, sea-level rise, and climatic variability. Most previous research relies on deterministic or numerical models that, while physically interpretable, often lack adaptability and generalization across different coastal environments, whereas machine learning approaches are commonly limited to classification or factor analysis rather than direct spatiotemporal prediction. To address these limitations, this research proposes a Convolutional Long Short-Term Memory (ConvLSTM)-Net model to predict coastal erosion along the northern coast of Indramayu, West Java, Indonesia using the Landsat dataset. By integrating convolutional layers for spatial feature extraction with recurrent units for temporal dependency modeling, the proposed approach enables automated and scalable forecasting of shoreline evolution, offering a data-driven decision-support tool for coastal management and erosion mitigation in vulnerable coastal regions. The results show the ConvLSTM-Net model achieving a Dice score of around 99% and an Intersection over Union (IoU) of around 98%. These findings suggest that integrating convolutional and recurrent architectures effectively captures complex coastal transformations, providing a robust tool for long-term coastal management and disaster mitigation.
Keywords:
coastal erosion, ConvLSTM, ConvLSTM-Net, LSTM, U-Net, prediction, spatiotemporalDownloads
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Copyright (c) 2026 Willy Boen, Goldwin Hoxenlly, Ivander Hanson Setyawan, Edy Irwansyah, Vini Indriasari
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