CLASSIFICATION OF NATURAL DISASTERS IN WEST SEMARANG BASED ON WEATHER DATA USING DEEP LEARNING
DOI:
https://doi.org/10.33480/jitk.v10i4.6147Keywords:
gated recurrent unit, long short-term memory, natural disaster, recurrent neural network, west semarangAbstract
Natural disasters like floods, landslides, and fires pose serious threats to both life and mental well-being, especially in vulnerable areas like West Semarang, which frequently experiences extreme weather. To mitigate these risks, an accurate classification system is essential for timely prevention and response. This study compares the performance of three neural network models—Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying natural disasters using weather data. LSTM and GRU are particularly effective for handling long-term dependencies and addressing vanishing gradient problems common in time series data. Data for the study comes from the Semarang City Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), spanning 2019 to 2022. The models achieved a high accuracy of 95.8%, but this is due to an imbalanced dataset—70 records of natural disasters versus 1377 without—resulting in classification favoring "no disaster." Among the models, LSTM performed the best, reaching optimal accuracy in just 20.0671 seconds per epoch. This suggests LSTM is the most effective model for this classification task.
Downloads
References
D. Arifin, “Kota Semarang Dikepung Banjir Akibat Cuaca Ekstrem.” Accessed: Jun. 13, 2024. [Online]. Available: https://bnpb.go.id/berita/kota-semarang-dikepung-banjir-akibat-cuaca-ekstrem
M. Dimitrova and M. Snair, “Classifying disaster risk reduction strategies: conceptualizing and testing a novel integrated approach,” Global Health, vol. 20, no. 1, Dec. 2024, doi: 10.1186/s12992-023-01006-8.
A. S. Albahri et al., “A systematic review of trustworthy artificial intelligence applications in natural disasters,” Computers and Electrical Engineering, vol. 118, p. 109409, Sep. 2024, doi: 10.1016/J.COMPELECENG.2024.109409.
S. K. Abid et al., “Toward an integrated disaster management approach: How artificial intelligence can boost disaster management,” Nov. 01, 2021, MDPI. doi: 10.3390/su132212560.
U. Nation, “Goal 13: Take urgent action to combat climate change and its impacts.” Accessed: May 05, 2024. [Online]. Available: https://www.un.org/sustainabledevelopment/climate-change/
L. Wu, D. Ma, and J. Li, “Assessment of the Regional Vulnerability to Natural Disasters in China Based on DEA Model,” Sustainability (Switzerland), vol. 15, no. 14, Jul. 2023, doi: 10.3390/su151410936.
Y. Ma, G.-F. Feng, and C.-P. Chang, “From traditional innovation to green innovation: How an occurrence of natural disasters influences sustainable development?,” Sustainable Development, vol. 32, no. 3, pp. 2779–2796, 2024, doi: https://doi.org/10.1002/sd.2802.
V. Linardos, M. Drakaki, P. Tzionas, and Y. L. Karnavas, “Machine Learning in Disaster Management: Recent Developments in Methods and Applications,” Jun. 01, 2022, MDPI. doi: 10.3390/make4020020.
N. S. Y. Tan, M. L. Tham, S. Y. Chua, and Y. L. Lee, “Accurate and Fast Classification of Natural Disasters using CNN-LSTM and Inference Acceleration,” Journal of Communications Software and Systems, vol. 20, no. 1, pp. 58–68, 2024, doi: 10.24138/jcomss-2023-0129.
M. Krichen, M. S. Abdalzaher, M. Elwekeil, and M. M. Fouda, “Managing natural disasters: An analysis of technological advancements, opportunities, and challenges,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 99–109, Jan. 2024, doi: 10.1016/J.IOTCPS.2023.09.002.
M. Paramesha, N. Liladhar Rane, and J. Rane, “Artificial Intelligence, Machine Learning, Deep Learning, and Blockchain in Financial and Banking Services: A Comprehensive Review,” 2024, doi: 10.5281/zenodo.12826933.
R. Jena, S. P. Naik, B. Pradhan, G. Beydoun, H. J. Park, and A. Alamri, “Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM),” International Journal of Disaster Risk Reduction, vol. 66, p. 102642, Dec. 2021, doi: 10.1016/J.IJDRR.2021.102642.
M. Waqas and U. W. Humphries, “A critical review of RNN and LSTM variants in hydrological time series predictions,” MethodsX, vol. 13, p. 102946, Dec. 2024, doi: 10.1016/J.MEX.2024.102946.
Z. Zainuddin, E. A. P. Akhir, and M. H. Hasan, “Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 870–878, 2021, doi: 10.11591/eei.v10i2.2036.
C. Ubal, G. Di-Giorgi, J. E. Contreras-Reyes, and R. Salas, “Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks,” Mach Learn Knowl Extr, vol. 5, no. 4, pp. 1340–1358, 2023, doi: 10.3390/make5040068.
K. K. Ramachandran, “The Evolution of Recurrent Neural Networks in Handling Long-Term Dependencies in Sequential Data,” 2024. [Online]. Available: https://iaeme.com/Home/issue/IJNNDL?Volume=1&Issue=1
S. Y. Jhin, S. Kim, and N. Park, “Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, in KDD ’24. ACM, Aug. 2024, pp. 1234–1245. doi: 10.1145/3637528.3671969.
T. Perumal, N. Mustapha, R. Mohamed, and F. M. Shiri, “A Comprehensive Overview and Comparative Analysis on Deep Learning Models,” Journal on Artificial Intelligence, vol. 6, no. 1, pp. 301–360, 2024, doi: 10.32604/jai.2024.054314.
S. H. Noh, “Analysis of gradient vanishing of RNNs and performance comparison,” Information (Switzerland), vol. 12, no. 11, Nov. 2021, doi: 10.3390/info12110442.
N. Zucchet and A. Orvieto, “Recurrent neural networks: vanishing and exploding gradients are not the end of the story,” 2024. [Online]. Available: https://arxiv.org/abs/2405.21064
I. Wayan and A. Suranata, “Pengembangan Model Prediksi Curah Hujan di Kota Denpasar Menggunakan Metode LSTM dan GRU,” JSI, vol. 18, pp. 64–73, 2023.
R. Akbar, R. Santoso, and B. Warsito, “PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM),” Jurnal Gaussian, vol. 11, no. 4, pp. 572–579, Feb. 2023, doi: 10.14710/j.gauss.11.4.572-579.
H. Sharfina, P. Y. Utami, and I. Fakhruzi, “Prediksi Bencana Banjir Menggunakan Algoritma Deep Learning H2O Berdasarkan Data Curah Hujan,” 2023. [Online]. Available: http://jurnal.mdp.ac.id
G. K. Fordjour and A. J. Kalyanapu, “Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee,” 2024. [Online]. Available: https://arxiv.org/abs/2405.10375
L. Zhang, H. Qin, J. Mao, X. Cao, and G. Fu, “High temporal resolution urban flood prediction using attention-based LSTM models,” J Hydrol (Amst), vol. 620, p. 129499, 2023, doi: https://doi.org/10.1016/j.jhydrol.2023.129499.
M. Cho, C. Kim, K. Jung, and H. Jung, “Water Level Prediction Model Applying a Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) Method for Flood Prediction,” Water (Switzerland), vol. 14, no. 14, Jul. 2022, doi: 10.3390/w14142221.
G. Dudek, S. Smyl, and P. Pełka, “Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study,” Mar. 2022, [Online]. Available: http://arxiv.org/abs/2203.09170
B. P. B. Daerah, “Data Bencana.” Accessed: May 18, 2024. [Online]. Available: http://www.bpbd.semarangkota.go.id/pages/data-bencana
B. M. K. Geofisika, “DATA ONLINE - PUSAT DATABASE - BMKG.” Accessed: May 10, 2024. [Online]. Available: https://dataonline.bmkg.go.id/home
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nicholas Martin, Jason Permana, Tony Tony
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
