Flood Disaster Prediction Model Using Long Short-Term Memory (LSTM) in Pekalongan, Central Java.

https://doi.org/10.22146/jag.92417

Muhammad Asrofi(1*), Muhammad Rizqy Septyandy(2), Tito Latif Indra(3)

(1) Program Study of Geology, Departement of Geoscience, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
(2) Program Study of Geological Engineering, Faculty of Engineering, Universitas Mulawarman
(3) epartment of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
(*) Corresponding Author

Abstract


Pekalongan is located in the northern part of Java Island, directly adjacent to the sea in the north. Natural disasters that often occur in Pekalongan are floods, especially in the north of the area, which has a height of 0 meters above sea level. In addition, Pekalongan also has a relatively low land slope of around 0 – 5%, which makes it challenging to distribute water and construct drainage. This study aims to be able to perform predictive modeling of flood-prone areas for the next five years. This study used eight parameters: rainfall, elevation, slope, distance to the river, distance to the sea, groundwater table to surface, soil type, and land use. This research used the Long Short-Term Memory (LSTM) method to predict rainfall parameters using the Python programming language with Jupyter Notebook software. Later, the data will be used as training and test data. Training data testing and tests are conducted to find the minimum failure or error value. The weight scoring method is carried out on each parameter to indicate areas with a high flood vulnerability level. The results showed that Pekalongan has a medium to very high vulnerability level, with a dominant high vulnerability level. The very high level of vulnerability is prevalent in the northern part of the research area, which is directly adjacent to the sea or in the North Pekalongan District. Floods that occur in the northern part of the study area are not only due to high levels of rainfall but can also occur due to the inflow of seawater towards the mainland resulting from high tides and high sea waves. The southern region of the study area has a smaller vulnerability level than the northern region, which has a medium to high vulnerability level.

Keywords: Flood ∙ Hazard ∙ Precipitation ∙ LSTM ∙ Rainfall


Keywords


Flood;Hazard;Precipitation;LSTM;Rainfall

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References

Ari Setiaji, Romy & Chandra, Andhika & Nahli, Kiddy & Radityo, Daniel & Arifullah, Ery. (2016). The Stratigraphic Significance of Glossifungites Ichnofacies in Cipari Area, Central Java.

Azis, M. F. A., Darari, F., & Septyandy, M. R. (2020). Time series analysis on earthquakes using EDA and machine learning. 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020, 405–412 Bemmelen, V. R. W. (1949). The Geology of Indonesia. General Geology of Indonesia and Adjacent Archipelagoes. In Government Printing Office, The Hague (pp. 545–547; 561–562).

BPS Pekalongan. (2021). Pekalongan in Numbers. BPS Kota Pekalongan.

Bretschneider, C. L., & Wybro, P. G. (1977). Tsunami inundation prediction. In: Proc. Fifteenth Coastal Engng. Conf. (Hawaii Univ., U.S.A.: Jul.11-17, 1976), 1, New Yo, 1006–1024.

Condon, W. H. (1975). Geological Map of Banjarnegara and Pekalongan, Java. Scale 1: 100000.

Hall, T., Brooks, H. E., & Doswell, C. A. (1999). Precipitation forecasting using a neural network. Weather and Forecasting, 14(3), 338–345.

Hardiyawan, M. (2012). Kerentanan Wilayah Terhadap Banjir Rob Di Pesisir Pekalongan. Kerentanan Wilayah Terhadap Banjir Rob Di Pesisir Kota Pekalongan, 20294641.

Hartono, G. (2010). Produk Batuan Gunung Api Tersier Jawa Tengah the Role of Paleovolcanism in the Tertiary Volcanic Rock Product Setting At Mt . Gajahmungkur , Wonogiri , Central Jawa the Role of Paleovolcanism in the Tertiary Volcanic Rock Product Setting.

Hilmi, F., & Haryanto, I. (2008). Pola Struktur Regional Jawa Barat. Bulletin of Scientific Contribution, 6(1), 57–66.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory [J]. Neural Computation, 9(8), 1735–1780.

Kementrian Pekerjaan Umum dan Perumahan Rakyat. (2017). Modul Geologi dan HidrogeologiPelatihan Perencanaan Air Tanah 2017. Pusat Pendidikan Dan Pelatihan Sumber Daya Air DanKonstruksi, 76.

Kuligowski, R. J., & Barros, A. P. (1998). Experiments in short-term precipitation forecasting using artificial neural networks. Monthly Weather Review, 126(2), 470–482.

Kusumo, P., & Nursari, E. (2016). Zonasi Tingkat Kerawanan Banjir dengan Sistem Informasi Geografis pada DAS Cidurian Kab. Serang, Banten. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 1(1), 29–38.

Meena, B. L., & Agrawal, J. D. (2015). Tidal level forecasting using ANN. Procedia Engineering, 116, 607–614.

Moustris, K. P., Larissi, I. K., Nastos, P. T., & Paliatsos, A. G. (2011). Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resources Management, 25, 1979–1993.

National Disaster Management Agency (NDMA). (2019). Technical Module for Flood Risk Studies Preparation. October, 1–101.

Nugroho Adji, T., Nurjani, E., & Wicaksono, D. (2014). Zonasi Potensi Airtanah Dengan Menggunakan Beberapa Parameter Lapangan dan Pendekatan SIG di Daerah Kepesisiran. Laporan Akhir Penelitian, 1–30.

Rizki, M., Basuki, S., & Azhar, Y. (2020). Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(LSTM) Untuk Prediksi Curah Hujan Kota Malang. Jurnal Repositor, 2(3), 331.

Rosyidie, A. (2013). Banjir: Fakta dan Dampaknya, Serta Pengaruh dari Perubahan Guna Lahan. Journal of Regional and Planning, 24(3), 241.

Salim, M. A. (2018). Penanganan banjir dan rob di wilayah Pekalongan. Jurnal Teknik Sipil, 11, 15–23.

Supriyadi, E. (2021). Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (Lstm). Jurnal Meteorologi Dan Geofisika, 21(2), 55.

Van Zuidam, R. A. (1985). Aerial photo-interpretation in terrain analysis and geomorphologic mapping. The Netherlands. Smith Publishers.

Wiranda, L., & Sadikin, M. (2019). Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 8(3), 184–196.



DOI: https://doi.org/10.22146/jag.92417

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