Rantini, Dwi and Kruba, Rumaisa and Haditiar, Yudi and Ikhwan, Muhammad and Jati Wijaya, Yusuf and Ismanto, Aris and Yandra, Muhammad Mahdy and Rahman, Hafiz and Ramadan, Arip and Othman, Fazidah (2026) Predicting upwelling dynamics in the South Sea of Java, Indonesia: A deep learning approach with ConvLSTM and 3D-CNN. MethodsX, 16.

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Abstract

Oceans exhibit complex dynamics influenced by climate change, anthropogenic activities, and natural phenomena. Understanding these dynamics is critical for ensuring the sustainability of marine environments and their optimal utilization. This research aims to study and monitor upwelling phenomena in the South Sea of Java. Upwelling, the exchange of nutrient-rich, cold water from deeper layers to the surface, enhances marine biological productivity; Sea Surface Temperature (SST) serves as a key indicator for its detection. To achieve these objectives, this study employs both ConvLSTM and 3D-CNN. ConvLSTM, a deep learning architecture that integrates convolutional structures within LSTM units, effectively captures spatiotemporal dependencies in sequential data. 3D-CNN, a deep learning model extending traditional 2D convolutional neural networks, processes volumetric data, enabling the extraction of spatial features across three dimensions. Analysis reveals that ConvLSTM outperforms 3D-CNN in modeling upwelling data in the South Sea of Java. This is evidenced by lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The ConvLSTM method was then used for forecasting, and the results were validated with data obtained from local fishermen regarding their fishing expeditions. Visual analysis confirms that the ConvLSTM method accurately models upwelling data in the South Sea of Java with fishermen's schedules.ConvLSTM and 3D-CNN methods were comparatively evaluated for modeling Sea Surface Temperature (SST) data, considering wind speed, sea surface salinity, and the El Niño-Southern Oscillation (ENSO) phase as influential factors.Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, the ConvLSTM method exhibited lower values, indicating superior performance compared to the 3D-CNN approach. Specifically, RMSE and MAE values for ConvLSTM were 0.4161 and 0.3017, respectively, while for 3D-CNN, the corresponding values were 0.6095 and 0.4259.Upwelling data forecasting results were validated against local fishermen's schedules, with data collected in July 2022. Visual inspection confirmed alignment between the forecasted upwelling patterns and the fishermen's activity. © 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/

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