Prediksi Banjir menggunakan ANFIS-PCA sebagai Peringatan Dini Bencana Banjir
Sari
ABSTRAK
Di antara kejadian bencana yang terjadi di Indonesia, 76 persen terdiri dari bencana hidrometeorologi seperti banjir, badai, longsor, dan kebakaran hutan. Provinsi DKI Jakarta sebagai daerah perkotaan sangat rentan terhadap banjir. Persamaan matematis yang kompleks dapat digunakan untuk memodelkan kejadian banjir secara fisik. Sistem pembelajar (machine learning) adalah sistem yang merancang dan mengembangkan algoritma yang menggunakan data historis untuk melakukan prediksi banjir. Dengan menggunakan data ini, sistem pembelajar dapat menghasilkan nilai probabilitas dasar, yang sangat membantu sistem prediksi, memberikan solusi yang lebih hemat biaya dan kinerja yang lebih baik. Prediksi yang akurat dan tepat dapat membantu strategi pengelolaan sumber daya air, analisis kebijakan dan rekomendasi serta pemodelan evakuasi lebih lanjut. Penelitian ini akan dibahas tentang Perancangan Sistem Peringatan Dini Banjir berbasis Ensemble Machine Learning sebagai mitigasi bencana banjir. Hasil dari penelitian menunjukkan nilai RMSE dari algoritma ANFIS – PCA adalah sebesar 0.12 dan koefisen korelasi (R2) sebesar 0.856.
Kata kunci: Prediksi Banjir, Machine Learning, ANFIS, ANFIS – PCA
ABSTRACT
The nation of Indonesia is prone to disaster, with 76% of natural disasters being hydrometeorological, such as floods, landslides, tropical cyclones, and droughts. Flood occurrences can be physically modeled using complex mathematical equations. Machine Learning serves as a system for designing and developing algorithms that can predict flood events using historical data. Machine learning systems can leverage existing data to produce underlying probability values, making significant contributions to prediction systems that offer better performance and cost-effective solutions. Accurate predictions contribute to water resource management strategies, policy recommendations, and further evacuation modeling. This research will discuss an Early Warning Flood System design based on Ensemble Machine Learning as a flood disaster mitigation measure. The research results show that the RMSE value and coefficient correlation (R2) for the ANFIS - PCA algorithm are 0.12 and 0.856, respectively.
Keywords: Flood Early Warning, Machine Learning, ANFIS, ANFIS – PCA
Kata Kunci
Teks Lengkap:
PDFReferensi
Agarwal, S. (2014). Data mining: Data mining concepts and techniques. In Proceedings 2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013. https://doi.org/10.1109/ICMIRA.2013.45
Alabrah, A. (2023). An Improved CCF Detector to Handle the Problem of Class Imbalance with Outlier Normalization Using IQR Method. Sensors, 23(9). https://doi.org/10.3390/s23094406
Ata, R., & Kocyigit, Y. (2010). An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications, 37(7), 5454–5460. https://doi.org/10.1016/j.eswa.2010.02.068 awscenter@bmkg.go.id. (n.d.). BMKG.
Badan Penanggulangan Bencana Daerah (BPBD). (2013). Disaster Management Plan of DKI Jakarta Province 2013-2017. 50–63.
Caesarendra, W., & Pamungkas, D. (2017). EMG based Classification of Hand Gestures using PCA and ANFIS. 18–23.
Chang, F. J., & Chen, Y. C. (2001). A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology, 245(1–4), 153–164. https://doi.org/10.1016/S0022-1694(01)00350-X
Chatterjee, S., & Das, A. (2020). A novel systematic approach to diagnose brain tumor using integrated type-II fuzzy logic and ANFIS (adaptive neuro-fuzzy inference system) model. Soft Computing, 24(15), 11731–11754. https://doi.org/10.1007/s00500-019-04635-7
Dineva, A., Várkonyi-Kóczy, A. R., & Tar, J. K. (2014). Fuzzy expert system for automatic wavelet shrinkage procedure selection for noise suppression. INES 2014 - IEEE 18th International Conference on Intelligent Engineering Systems, Proceedings, 163–168. https://doi.org/10.1109/INES.2014.6909361
Edvin Aldrian, Mimin Karmini, B. (2011). Adaptasi dan Mitigasi Perubahan Iklim di Indonesia.
Firat, M., & Turan, M. E. (2010). Monthly river flow forecasting by an adaptive neuro-fuzzy inference system. Water and Environment Journal, 24(2), 116–125. https://doi.org/10.1111/j.1747-6593.2008.00162.x
Geofisika, D., Meteorologi, D., Matematika, F., Ilmu, D., & Alam, P. (2012). PENENTUAN BATAS AMBANG CURAH HUJAN PENYEBAB BANJIR (Studi Kasus DAS Ciliwung Hulu) ARRIDHA DARA KOMEJI.
Ghalkhani, H., Golian, S., Saghafian, B., Farokhnia, A., & Shamseldin, A. (2013). Application of surrogate artificial intelligent models for real-time flood routing. Water and Environment Journal, 27(4), 535–548. https://doi.org/10.1111/j.1747-6593.2012.00344.x
Giarno, G., Saputra, A. H., & Rachmawardani, A. (2022). Optimalisasi Edukasi Informasi Geohidrometeorologi Untuk Masyarakat Perkotaan (Studi Kasus: Kelurahan Jurang Mangu Timur, Kecamatan Pondok Aren, Kota Tangerang Selatan, Banten). To Maega : Jurnal Pengabdian Masyarakat, 5(3), 554. https://doi.org/10.35914/tomaega.v5i3.1294
Gustari, I., Hadi, T. W., Hadi, S., & Renggono, F. (2012). Akurasi Prediksi Curah Hujan Harian Operasional Di Jabodetabek : Perbandingan Dengan Model Wrf. Jurnal Meteorologi Dan Geofisika, 13(2), 119–130. https://doi.org/10.31172/jmg.v13i2.126
https://data.jakarta.go.id/dataset /rekapitulasi-kejadian-banjir-pertahun/resource). (n.d.). Pemprov DKI Jakarta.
Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An Overview of Principal Component Analysis. Journal of Signal and Information Processing, 04(03), 173–175. https://doi.org/10.4236/jsip.2013.43b031
Kim, S., Matsumi, Y., Pan, S., & Mase, H. (2016). A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan. Ocean Engineering, 122, 44–53. https://doi.org/10.1016/j.oceaneng.2016.06.017
Mauliate, H., Gromiko, A., Unggul, U. E., Tomang, T., & Jeruk, K. (2017). Evaluasi ketangguhan kota terhadap bencana banjir. Jurnal Planesa, 8(1), 41–53.
Mosavi, A., & Edalatifar, M. (2019). A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration. In Lecture Notes in Networks and Systems (Vol. 53). Springer International Publishing. https://doi.org/10.1007/978-3-319-99834-3_31
Nguyen, P. K. T., Chua, L. H. C., Talei, A., & Chai, Q. H. (2018). Water level forecasting using neuro-fuzzy models with local learning. Neural Computing and Applications, 30(6), 1877–1887. https://doi.org/10.1007/s00521-016-2803-9
Ortiz-García, E. G., Salcedo-Sanz, S., & Casanova-Mateo, C. (2014). Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmospheric Research, 139, 128–136. https://doi.org/10.1016/j.atmosres.2014.01.012
Premalatha, G., & Bai, V. T. (2022). Wireless IoT and Cyber-Physical System for Health Monitoring Using Honey Badger Optimized Least-Squares Support-Vector Machine. Wireless Personal Communications, 124(4), 3013–3034. https://doi.org/10.1007/s11277-022-09500-9
Presiden Republik Indonesia. (2020). Peraturan Presiden Republik Indonesia Nomor 87 tahun 2020 tentang Rencana Induk Penanggulangan Bencana Tahun 2020-2044. Database Peraturan BPK RI, 87, 1–31. https://peraturan.bpk.go.id/Home/Details/146481/perpres-no-87-tahun-2020
Rachmawardani, A., Wijaya, S. K., & Shopaheluwakan, A. (2022). Sistem Peringatan Dini Banjir Berbasis Machine Learning: Studi Literatur. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 6(6), 188–198. https://doi.org/10.46880/jmika.vol6no2.pp188-198
Smith, L. (2006). A tutorial on PCSA. Department of Computer Science, University of Otago., 12–28.
Solomatine, D., See, L. M., & Abrahart, R. J. (2008). Data-Driven Modelling: Concepts, Approaches and Experiences. Practical Hydroinformatics, 17–30. https://doi.org/10.1007/978-3-540-79881-1_2
Taherei Ghazvinei, P., Darvishi, H. H., Mosavi, A., Bin Wan Yusof, K., Alizamir, M., Shamshirband, S., & Chau, K. W. (2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering Applications of Computational Fluid Mechanics, 12(1), 738–749. https://doi.org/10.1080/19942060.2018.1526119
Talei, A., & Chua, L. H. C. (2012). Influence of lag time on event-based rainfall-runoff modeling using the data driven approach. Journal of Hydrology, 438–439, 223–233. https://doi.org/10.1016/j.jhydrol.2012.03.027
DOI: https://doi.org/10.26760/elkomika.v12i2.335
Refbacks
- Saat ini tidak ada refbacks.
_______________________________________________________________________________________________________________________
ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638
diterbitkan oleh :
Teknik Elektro Institut Teknologi Nasional Bandung
Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124
Kontak : Tel. 7272215 (ext. 206) Fax. 7202892
Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________
Statistik Pengunjung
Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.