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Article | Green Intelligent Systems and Applications | Volume 5 - Issue 2 - 2025 | 164-180 | https://doi.org/10.53623/gisa.v5i2.880
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Temporal Analysis of Land Subsidence in DKI Jakarta Using the Long Short-Term Memory (LSTM) Model

Author(s): Heni Fitriany 1 , Panca Dewi Pamungkasari 1 , Yunan Fauzi Wijaya Wijaya 1 , Muhammad Fauzan Azhiman 1 , Yasuhito Nagase Nagase 2 , Joko Widodo 3
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1 Universitas Nasional Faculty of Communication and Information Technology, Jakarta, Indonesia
2 Department of Earth and Environmental Sciences, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
3 Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia

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Green Intelligent Systems and Applications

Volume 5 - Issue 2 - 2025

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This research investigated temporal patterns of land subsidence in DKI Jakarta by applying a Long Short-Term Memory (LSTM) model to deformation measurements derived from Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) observations acquired between 2017 and 2021. Because the original PS-InSAR time series was characterized by uneven acquisition intervals, the deformation records were first resampled into a uniform 11-day sequence to obtain a consistent temporal structure for modeling. Preprocessing steps, comprising outlier handling, temporal resampling, and feature normalization, were performed to ensure that the model could capture deformation behavior reliably. The LSTM architecture employed three stacked recurrent layers and was trained using the Adam optimizer with Smooth L1 Loss and an early-stopping strategy. Model evaluation demonstrated excellent agreement between predicted and observed deformation, yielding R² = 1.000, MSE = 0.104, RMSE = 0.322 mm, and KGE = 0.998. Compared with a previously developed Random Forest model (R² = 0.9995, RMSE = 0.3314 mm), the LSTM approach exhibited more stable temporal learning and was better suited for long-horizon deformation forecasting. Spatial analysis revealed that northern Jakarta, particularly Cengkareng, Tanjung Priok, and Pantai Indah Kapuk, continued to experience the greatest cumulative subsidence (> −30 mm), whereas areas in the south, such as Jagakarsa and Kebayoran Baru, showed minimal deformation (< −5 mm), aligning with known geological and anthropogenic conditions. Overall, integrating PS-InSAR time series with an LSTM framework provided a more coherent and temporally consistent method for characterizing subsidence behavior in Jakarta. The outcomes of this study offered a scientific basis for developing intelligent monitoring tools to support mitigation efforts and sustainable urban planning in regions affected by land subsidence.

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SUBMITTED: 06 November 2025
ACCEPTED: 10 December 2025
PUBLISHED: 16 December 2025
SUBMITTED to ACCEPTED: 35 days
DOI: https://doi.org/10.53623/gisa.v5i2.880

Cite this article
Fitriany, H., Pamungkasari, P. D. ., Wijaya, Y. F. W., Azhiman, M. F. ., Nagase, Y. N., & Widodo, J. . (2025). Temporal Analysis of Land Subsidence in DKI Jakarta Using the Long Short-Term Memory (LSTM) Model. Green Intelligent Systems and Applications, 5(2), 164–180. https://doi.org/10.53623/gisa.v5i2.880
Keywords
land subsidence LSTM PS-InSAR Deep Learning temporal prediction
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