Multivariate Time-Series Flood Prediction Using LSTM Networks and GIS-Based Visualization of Meteorological Data from BMKG

DOI

10.2991/978-2-38476-565-2_4How to use a DOI?

Keywords

BMKG Meteorological Data; Deep Learning; Flood Forecasting; GIS Visualization; LSTM Networks

Abstract

Accurate and timely flood forecasting is essential for reducing socio-economic losses in coastal urban areas. This study proposes a multivariate time-series forecasting framework for predicting short-term rainfall intensity as an indicator of potential flooding in Baubau City, Southeast Sulawesi. The research utilizes fifteen years (2009-2024) of local meteorological data obtained from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG). A deep learning approach based on Long Short-Term Memory (LSTM) and hybrid CNN-LSTM architectures is employed to capture temporal dependencies among multiple atmospheric variables, enabling 1-7 day ahead rainfall forecasts. The modeling process includes systematic data cleaning, normalization, and temporal feature extraction to enhance predictive accuracy. The performance of the proposed framework is evaluated against conventional statistical and machine-learning baselines using standard error metrics and efficiency coefficients. Furthermore, the forecasting results are spatially integrated within QGIS to generate flood-risk maps, facilitating visual interpretation and decision-making support for local disaster management authorities. Experimental results demonstrate that the LSTM-based model effectively captures complex temporal interactions in the meteorological dataset, outperforming baseline models in both accuracy and reliability. This integration of deep learning and GIS provides a practical, data-driven foundation for improving flood early- warning systems and strengthening adaptive planning in coastal regions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rando Rando
AU  - Agusman Agusman
PY  - 2026
DA  - 2026/04/30
TI  - Multivariate Time-Series Flood Prediction Using LSTM Networks and GIS-Based Visualization of Meteorological Data from BMKG
BT  - Proceedings of the 2nd International Conference on Social Environment Diversity (ICOSEND 2025)
PB  - Atlantis Press
SP  - 18
EP  - 25
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-565-2_4
DO  - 10.2991/978-2-38476-565-2_4
ID  - Rando2026
ER  -