Rainfall Forecasting Using Backpropagation Neural Network
DOI:
https://doi.org/10.25126/jitecs.2017229Abstract
Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815;Â 0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.References
Anifa, I.A., Regasari, R., Marji: Peramalan Permintaan Beras Menggunakan Backpropagation Dengan Inisialisasi Bobot Nguyen-Widrow. Universitas Brawijaya, Malang (2015).
Buono, A., Kurniawan, A., Komputer, D.I.: Peramalan Awal Musim Hujan Menggunakan Jaringan Syaraf Tiruan Backpropagation Levenberg-Marquardt. 2012, 15–16 (2012).
Hashim, R., Roy, C., Motamedi, S., Gocic, M., Lee, S.C., Cheng, S.: Selection of meteoro-logical parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmos. Res. 171, 21–30 (2015).
Heaton, J.: Introduction to Neural Network for Java. Heaton Research, Inc, St. Louis (2005).
Huda, F. Al, Mahmudy, W.F., Tolle, H.: Android Malware Detection Using Backpropagation Neural Network. TELKOMNIKA. 4, (2015).
Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13, 1413–1425 (2008).
Iriany, A., Mahmudy, W.F., Sulistyono, A.D., Nisak, S.C.: GSTAR-SUR Model for Rainfall Forecasting in Tengger Region , East Java. 1st Int. Conf. Pure Appl. Res. Univ. Muham-madiyah Malang, 21-22 August. 1–8 (2015).
Kannan, M., Prabhakaran, S., Ramachandran, P.: Rainfall Forecasting Using Data Mining Technique. Int. J. Eng. Technol. 2, 397–400 (2010).
Luk, K.C., Ball, J.E., Sharma, A.: A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 227, 56–65 (2000).
Mazur, M.: A Step by Step Backpropagation Example, http://mattmazur.com.
Nasseri, M., Asghari, K., Abedini, M.J.: Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst. Appl. 35, 1415–1421 (2008).
Oxford Dictionaries: Concise Oxford English Dictionary. Oxford University Press, Oxford-shire, UK (2009).
Sedki, A., Ouazar, D., El Mazoudi, E.: Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting. Expert Syst. Appl. 36, 4523–4527 (2009).
Sumi, S.M., Zaman, M.F., Hirose, H.: A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. Int. J. Appl. Math. Comput. Sci. 22, 841–854 (2012).
Vamsidhar, E., Varma, K., Rao, P., Satapati, R.: Prediction of rainfall using backpropagation neural network model. Int. J. Comput. Sci. Eng. 2, 1119–1121 (2010).
Wahyuni, I., Mahmudy, W.F., Iriany, A.: Rainfall Prediction in Tengger Region-Indonesia Using Tsukamoto Fuzzy Inference System. 1th Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. 1–11 (2016).
Yohannes, E., Mahmudy, W.F., Rahmi, A.: Penentuan Upah Minimum Kota Berdasarkan Tingkat Inflasi Menggunakan Backpropagation Neural Network ( BPNN ). J. Teknol. Inf. dan Ilmu Komput. 2, 34–40 (2015).
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
