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Multivariate Time-Series Flood Prediction Using LSTM Networks and GIS-Based Visualization of Meteorological Data from BMKG Rando (a*), Agusman (a) Corresponding Author: Rando Rando

Question from Mr. Dr. Nadir La Djamudi, S.Pd., M.Pd 2025.12.16 10:23:21 I am Dr. Nadir La Djamudi from Universitas Muhammadiyah Buton, Baubau City. Although I am a linguist, I am very interested in your research topic, ^Multivariate Time-Series Flood Prediction Using LSTM Networks and GIS-Based Visualization of Meteorological Data from BMKG,^ because I live in Baubau City. My question is: What are the contributions of your research findings for Baubau City, both at present and in the future? Thank you.

Reply from Mr. Rando Rando 2025.12.16 11:41:34 The findings of this research provide immediate contributions for Baubau City by delivering a data-driven flood forecasting and spatial visualization framework that supports early warning and localized risk identification. In the future, the proposed integration of deep learning and GIS can serve as a scalable decision-support tool for urban flood management, enabling improved planning, infrastructure adaptation, and disaster preparedness as hydrometeorological risks increase.

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