Yasin, Hasbi

English

Diponegoro University Institutional Repository

ScopusDocument details  3 of 18  Feed Forward Neural Network Modeling for Rainfall Prediction (Conference Paper)(Open Access),  ,   Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang, IndonesiaAbstractPrediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neuralnetwork which has three layers for processing. In time series prediction, including in case of rainfall data, the inputlayer is the past values of the same series up to certain lag and the output layer is the current value. Beside a fewlagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. Theautocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In thesecond layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic anddifferentiable. Processing is done by the weighted summing of the input variables and transfer process in the hiddenlayer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods areused to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the lastlayer. In each optimization method, the looping process is performed several times in order to get the most suitableresult in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria.The least of in-sample and out-sample predictions from the repeating results been the base of choosing the bestoptimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten.Simulation results give a consecution that the more complex architecture is not guarantee the better prediction. ©The Authors, published by EDP Sciences, 2018.SciVal Topic Prominence Topic: Prominence percentile: 99.217Author keywordsFFNN Prediction Rainfall RMSEIndexed keywordsEngineeringcontrolled terms:Autocorrelation Backpropagation algorithms Information systems Information useMean square error RainEngineeringuncontrolled termsAutocorrelation functions Complex architectures FFNNGradient-based optimization method Least mean square error Partial autocorrelation functionRMSE Time series predictionEngineering mainheading:ForecastingFunding details◅ Back to results ◅ Previous  ▻Next Export  Download  Print  E-mail  Save to PDF ⋆ Add to List  ▻More...View at PublisherE3S Web of ConferencesVolume 73, 21 December 2018, Article number 050173rd International Conference on Energy, Environmental and Information System, ICENIS 2018;Semarang; Indonesia; 14 August 2018 through 15 August 2018; Code 143635Hasbi, Y.  Budi, W. Rukun, S.  View references (9)Artificial neural network | Wavelet | Flood forecastingPlumX MetricsUsage, Captures, Mentions,Social Media and Citationsbeyond Scopus.Metrics Cited by 0 documentsInform me when this documentis cited in Scopus: Related documents,  , (2018) Journal of Theoretical andApplied Information Technology, (2017) Lecture Notes in ElectricalEngineering,  , (2015) Journal of Water Supply:Research and Technology - AQUA Find more related documents inScopus based on:❓Set citation alert ▻ ▻Set citation feedGradient based optimization incascade forward neural networkmodel for seasonal data Warsito, B. Santoso, R. Yasin,H.Heavy rainfall forecasting modelusing artificial neural network forflood prone area Sulaiman, J. Wahab, S.H.Application of generalizedregression neural network andsupport vector regression formonthly rainfall forecasting inwestern Jilin Province, China Lu, W. Chu, H. Zhang, Z.View all related documents basedon references ▻Authors  ▻KeywordsReferences (9)Funding sponsor Funding number Acronym101-24/UN7.P4.3/PP/2018Funding textThe authors would like to thank the Ministry of Research, Technology and High Education Republic of Indonesiawhich has provided research grants of PDUPT Research Grant Program No. 101-24/UN7.P4.3/PP/2018 date 5Pebruary 2018.Asadi, S., Shahrabi, J., Abbaszadeh, P., Tabanmehr, S.(2013) Neurocomputing, 121, pp. 470-480.  .doi: 10.1016/j.neucom.2013.05.023Benmahdjoub, K., Ameur, Z., Boulifa, M.(Open Access)(2013) Energy Procedia, 36, pp. 1138-1146.  .doi: 10.1016/j.egypro.2013.07.129Cigizoglu, H.K., Askin, P., Ozturk, A., Gurbuz, A., Ayhan, O., Yildiz, M., Ucar, I.Artificial neural network models in rainfall-runoff modelling of Turkish rivers(2008) International Congress on River Basin Management, pp. 560-571.  . Kim, S., Seo, Y., Lee, C.-J.(Open Access)(2016) Procedia Engineering, 154, pp. 1231-1236.  .doi: 10.1016/j.proeng.2016.07.442Lin, G.-F., Wu, M.-C.(2009) Journal of Hydrology, 375 (3-4), pp. 450-458.  .doi: 10.1016/j.jhydrol.2009.06.047ISSN: 22671242Source Type: Conference ProceedingOriginal language: EnglishDOI: 10.1051/e3sconf/20187305017Document Type: Conference PaperVolume Editors: Warsito B.,Hadiyanto,MaryonoPublisher: EDP Sciences ▻View in search results format     All Export  Print  E-mail  Save to PDF Create bibliography1A new hybrid artificial neural networks for rainfall-runoff process modelingCited 52 timesView at Publisher2Forecasting of rainfall using Time Delay Neural Network in Tizi-Ouzou (Algeria)Cited 9 timeshttp://www.sciencedirect.com/science/journal/18766102View at Publisher3Cited 6 times4Modeling of Rainfall by Combining Neural Computation and Wavelet TechniqueCited 2 timeshttp://www.sciencedirect.com/science/journal/18777058View at Publisher5A hybrid neural network model for typhoon-rainfall forecastingCited 63 timesView at Publisher  3 of 18  Mekanik, F., Imteaz, M.A., Gato-Trinidad, S., Elmahdi, A.(2013) Journal of Hydrology, 503, pp. 11-21.  .doi: 10.1016/j.jhydrol.2013.08.035Wang, Y.-M., Traore, S.(2009) International Journal of Physical Sciences, 4 (9), pp. 519-528.  .Yasin, H.Prediction of weekly rainfall in semarang city use support vector regressions(2014) Prosiding Seminar Internasional the 4th ISNPINSA, Hotel Patra Jasa Semarang Yasin, H., Dan Prahutama, A.Prediction of weekly rainfall in semarang city use support vector regression (svr) with quadratic loss function(2015) International Journal of Science and Engineering (IJSE), 9 (1).  Hasbi, Y.; Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang,Indonesia; email:  © Copyright 2019 Elsevier B.V., All rights reserved.6Multiple regression and Artificial Neural Network for long-term rainfall forecastingusing large scale climate modesCited 85 timesView at Publisher7Time-lagged recurrent network for forecasting episodic event suspended sediment loadin typhoon prone areaCited 20 timeshttp://www.academicjournals.org/ijps/PDF/pdf2009/September/Wang%20and%20Traore.pdf89 hasbiyasin17@gmail.com◅ Back to results ◅ Previous  ▻Next  Top of pageAbout ScopusWhat is ScopusContent coverageScopus blogScopus APIPrivacy mattersLanguage⽇本語に切り替える切换到简体中文切換到繁體中文Русский языкCustomer ServiceHelpContact us Copyright © . All rights reserved. Scopus® is a registered trademark of Elsevier B.V.We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the.↗Terms and conditions ↗Privacy policy ↗Elsevier B.V use of cookies ScopusSource detailsE3S Web of ConferencesScopus coverage years: from 2013 to PresentE-ISSN: 2267-1242Subject area: Earth and Planetary Sciences: General Earth and Planetary Sciences Energy: General EnergyEnvironmental Science: General Environmental Science  View all documents ▻  Save to source list Journal HomepageCiteScore 20180.52 SJR 20180.174 SNIP 20180.575 CiteScore CiteScore rank & trend CiteScore presets Scopus content coverageCalculated using data from 30 April, 2019CiteScore*CiteScore includes all available document types  0.52  =  Citation Count 2018 Documents 2015 -2017* =  Metrics displaying this icon are compiled according to , a collaboration betweenindustry and academia.2018  ▻905 Citations ▻1,747 Documents ▻View CiteScore methodology  ▻CiteScore FAQLast updated on 09 September, 2019CiteScoreTracker 20190.23  =  Citation Count 2019 Documents 2016 - 2018 = Updated monthly ▻1,256 Citations to date▻5,452 Documents to date ↗ Snowball MetricsCiteScore rankCategory Rank PercentileEarth andPlanetarySciences #119/182 34th Energy  #40/58 31st GeneralEarth andPlanetarySciencesGeneral ▻View CiteScore trends 🔗Add CiteScore to your siteAbout ScopusWhat is ScopusContent coverageScopus blogScopus APIPrivacy mattersLanguage⽇本語に切り替える切换到简体中文切換到繁體中文Русский языкCustomer ServiceHelpContact us Copyright © . All rights reserved. Scopus® is a registered trademark of Elsevier B.V.We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the.↗Terms and conditions ↗Privacy policy ↗Elsevier B.V use of cookies   PrefaceThis is the proceeding of the 3nd International Conference on Energy, Environmental andInformation System (ICENIS) 2018. The theme of ICENIS 2018 is “Strengthening Planning andImplementation Energy, Environment, and Information System Toward Low Carbon Society.ICENIS 2018 discussing the sustainability of the natural system, i.e energy management andpolicy, energy conservation, environmental education and planning, environmentalconservation, environmental technology, environmental health, pollution control, wastemanagement, green infrastructure and resilience, system information of supply chain anddecision support system. Moreover the ICENIS 2018 introducing discussion of social aspect inlow carbon society such as economic perspective, government, public policy and internationalrelation, formal informal worker, gender, media and culture developmentICENIS 2018 organized by school of Postgraduate Studies, Diponegoro University has beenconducted 14-15th august 2018. The conference has successfully performing forum totransferring and discussing research result among the researcher, students, government,private sector or industries. More than 390 participants and presenters from several countriessuch as Indonesia, Malaysia, Germany, Sudan, Australia, Japan, Libya have attended theconference to share their significant contribution in research related to energy, environmentand information system. This proceeding contains 334 selected paper from the conference.We would like to express our gratitude to all authors and members of scientific committee,reviewers and also organizing committee for their contribution to the success of theconference.Guest EditorsProf. HadiyantoDr. Eng. MaryonoDr. Budi Warsito

Hasil Review Artikel Prosiding Internasional C26: Feed Forward Neural Network Modeling for Rainfall Prediction

http://eprints.undip.ac.id/78434/1/C26-_Peer_Review_e3sconf_icenis18_05017.pdf

Hasil Review Artikel Prosiding Internasional C26: Feed Forward Neural Network Modeling for Rainfall Prediction

Abstract

Similar works

Full text

Available Versions

Diponegoro University Institutional Repository