skip to main content

Prediksi Kejadian Banjir dengan Ensemble Machine Learning Menggunakan BP-NN dan SVM

Flood Prediction with Ensemble Machine Learning using BP-NN and SVM

Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Indonesia

Received: 13 Nov 2018; Revised: 23 Jul 2019; Accepted: 29 Jul 2019; Available online: 4 Aug 2019; Published: 31 Jul 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
This study aims to examine the prediction of rainfall and river water debit using the Back Propagation Neural Network (BP-NN) method. Prediction results are classified using the Support Vector Machine (SVM) method to predict flooding. The parameters used to predict rainfall with BP-NN are minimum, maximum and average temperature, average relative humidity, sunshine duration, and average wind speed. The debit of Ular Pulau Tagor river is predicted by BP-NN. BPNN and SVM modeling using software R. Daily climate data from 2015-2017 were taken from three stations, namely Sampali climatology station, Kualanamu meteorological station, and Tuntung geophysics station. Prediction of river water debit is for 6 days and 30 days in the future. The best dataset is a 6 day prediction with a combination of 60% training and 40% testing. Flood prediction accuracy with SVM was 100% in predicting flood events for the next 6 days.
Keywords: ensemble machine learning; flood prediction, BP-NN and SVM; rainfall prediction; river water debit prediction
Funding: Kementerian Riset, Teknologi dan Pendidikan Tinggi Republik Indonesia

Article Metrics:

  1. D. Eni and F. J. Adeyeye, “Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria,” Journal of Geoscience and Environment Protection, vol. 3, no. 6, pp. 91-98, 2015
  2. S. Suhartono et al., “Ensemble Method Based On ANFIS-ARIMA For Rainfall Prediction,” in 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE), Langkawi, Malaysia, Sept. 2012, pp. 1-4
  3. N. A. Charaniya and S. V. Dudul, “Design of Neural Network Models for Daily Rainfall Prediction,” International Journal of Computer Applications, vol. 61, no. 14, pp. 23-27, 2013
  4. M. P. Darji, V. K Dabhi, and H. B. Prajapati, “Rainfall Forecasting using Neural Network: a Survey,” in 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 2015, pp. 706-713
  5. M. Mislana, H. Haviluddin, S. Hardwinarto, S. Sumaryono, and M. Aipassa, “Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan – Indonesia,” Procedia Computer Science, vol. 59, pp. 142-151, 2015
  6. BAPPEDA, “Aspek Geografi dan Demografi Daerah Sumatera Utara,” Bappeda Propinsi Sumatera Utara, 2012. [Online]. Available: http://bappeda.sumutprov.go.id/index.php/potensi-daerah/141-aspek-geografi-dan-demografi
  7. M. Rezaei, A. Motlaq, A. Mahmouei, and S. Mousavi, “River Flow Forecasting using Artificial Neural Network (Shoor Ghaen),” Ciência e Natura, vol. 37, pp. 207−215, 2015
  8. C. O. Maxwell, “Prediction of River Discharge Using Neural Networks,” Thesis, School of Computing and Informatics, University of Nairobi, Kenya, 2014. [Online]
  9. M. Yunianto et al., "Smart EWS: Sebelas Maret Early Warning Sistem Aplikasi Deteksi Dini Bencana Banjir Sungai Bengawan Solo Berbasis Android," in Seminar Nasional Geografi UMS VII, Solo, Indonesia, 2016, pp. 588-596
  10. T. M. Mitchell, Machine Learning. Mac Graw Hill, 1997
  11. C. Monteleoni et al., “Climate Informatics,” in Computational Intelligent Data Analysis for Sustainable Development, T. Yu, N. Chawla, and S. Simoff, Eds. Chapman and Hall/CRC, 2013, pp. 81-126
  12. D. Han, L. Chan, and N. Zhu, “Flood Forecasting using Support Vector Machines,” Journal of Hydroinformatics, vol. 9, no. 4, pp. 267-276, 2007
  13. N. S. Raghavendra and P. C. Deka, “Support Vector Machine Applications in the Field of Hydrology: A Review,” Applied Soft Computing, vol. 19, pp. 372–386, 2014
  14. A. A. Soebroto, I. Cholissodin, R. C. Wihandika, M. T. Frestantiya, and Z. El Arief, “Prediksi Tinggi Muka Air (TMA) Untuk Deteksi Dini Bencana Banjir Menggunakan SVR-TVIWPSO,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 2, pp. 79-86, 2015
  15. G. A. F. Alfarisy and W. Mahmudy, “Rainfall Forecasting in Banyuwangi using Adaptive Neuro Fuzzy Inference System,” Journal of Information, Technology and Computer Science, vol. 1, no. 2. pp 65-71, 2016
  16. S. K. T. Kuni and C. Mohandas, “Rainfall Runoff Modelling Using ANN and ANFIS,” in International Symposium on Integrated Water Resources Management (IWRM–2014), Kerala, India, Feb. 2014, pp. 9-16
  17. G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. Springer International Publishing, 2013
  18. N. Chamidah, W. Wiharto, and U. Salamah, “Pengaruh Normalisasi Data pada Jaringan Syaraf Tiruan Backpropagasi Gradient Descent Adaptive Gain (BPGDAG) untuk Klasifikasi,” ITSMART: Jurnal Teknologi dan Informasi, vol 1., no 1, pp. 28-33, 2012
  19. S. N. K. Kamarudin and A. A. Bakar, “Neural Network Algorithm Variants for Malaysian Weather. Second International Multi-Conference on Artificial Intelligence Technology,” in Soft Computing Applications and Intelligent System, S. A. Noah et al., Eds. Springer, 2013, pp. 121-134

Last update:

  1. PREDICTION OF SKINCARE SALES TURNOVER USING THE SUPPORT VECTOR METHOD AT THE WIDYA MSGLOW SIDOARJO COMPANY

    Oktaviana Isbirotin, Wiwiet Herulambang, Rahmawati Febrifyaning Tias, Rangsang, Ahmadi. JEECS (Journal of Electrical Engineering and Computer Sciences), 8 (2), 2023. doi: 10.54732/jeecs.v8i2.10
  2. Penerapan Machine Learning Untuk Prediksi Bencana Banjir

    Sulis Sandiwarno. Jurnal Sistem Informasi Bisnis, 14 (1), 2024. doi: 10.21456/vol14iss1pp62-76
  3. Watermelon ripeness detector using near infrared spectroscopy

    Edwin R. Arboleda, Kimberly M. Parazo, Christle M. Pareja. Jurnal Teknologi dan Sistem Komputer, 8 (4), 2020. doi: 10.14710/jtsiskom.2020.13744
  4. Comparison of coastal wind speed in Southeast Asian Countries using ANN backpropagation algorithm

    Syaharuddin, D R Muharani, M Ibrahim, V Mandailina. IOP Conference Series: Earth and Environmental Science, 1267 (1), 2023. doi: 10.1088/1755-1315/1267/1/012014
  5. Advancing Flood Disaster Mitigation in Indonesia Using Machine Learning Methods

    Hammam Riza, Eko Widi Santoso, Iwan Gunawan Tejakusuma, Firman Prawiradisastra. 2020 International Conference on ICT for Smart Society (ICISS), 2020. doi: 10.1109/ICISS50791.2020.9307561

Last update: 2024-12-09 21:45:06

  1. Advancing Flood Disaster Mitigation in Indonesia Using Machine Learning Methods

    Hammam Riza, Eko Widi Santoso, Iwan Gunawan Tejakusuma, Firman Prawiradisastra. 2020 International Conference on ICT for Smart Society (ICISS), 2020. doi: 10.1109/ICISS50791.2020.9307561