Loading…

Artificial Neural Network for Monthly Rainfall Rate Prediction

Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2017-03, Vol.180 (1), p.12057
Main Authors: Purnomo, H D, Hartomo, K D, Prasetyo, S Y J
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933
cites cdi_FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933
container_end_page
container_issue 1
container_start_page 12057
container_title IOP conference series. Materials Science and Engineering
container_volume 180
creator Purnomo, H D
Hartomo, K D
Prasetyo, S Y J
description Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.
doi_str_mv 10.1088/1757-899X/180/1/012057
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2563838942</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2563838942</sourcerecordid><originalsourceid>FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933</originalsourceid><addsrcrecordid>eNqFkE1LAzEQhoMoWKt_QRa8eFk3s0k3yUUopX5Aq-IHeAvpJsHUdbNmt0j_vakrFUHwNMPMM-_Ag9Ax4DPAnGfARizlQjxnwHEGGYYcj9gOGmwXu9uewz46aNslxgWjFA_Q-Th0zrrSqSq5MavwVboPH14T60My93X3Uq2Te-Vqq6oqNp1J7oLRruycrw_RXhy35ui7DtHTxfRxcpXObi-vJ-NZWlLMupSCWgCF3FLLtYACSMm0IFoRmnOqdUEXjIAtCKGlJpyXihLDlGZiYTgRhAzRSZ_bBP--Mm0nl34V6vhS5qOCcMIFzSNV9FQZfNsGY2UT3JsKawlYblzJjQa5USKjKwmydxUPT_tD55uf5PnD9BcmG20jmv-B_pP_CTOyd5Y</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2563838942</pqid></control><display><type>article</type><title>Artificial Neural Network for Monthly Rainfall Rate Prediction</title><source>Publicly Available Content Database</source><source>Free Full-Text Journals in Chemistry</source><creator>Purnomo, H D ; Hartomo, K D ; Prasetyo, S Y J</creator><creatorcontrib>Purnomo, H D ; Hartomo, K D ; Prasetyo, S Y J</creatorcontrib><description>Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.</description><identifier>ISSN: 1757-8981</identifier><identifier>EISSN: 1757-899X</identifier><identifier>DOI: 10.1088/1757-899X/180/1/012057</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Accuracy ; Artificial neural networks ; Forecasting ; Mathematical models ; Model accuracy ; Neural networks ; Rain ; Rainfall</subject><ispartof>IOP conference series. Materials Science and Engineering, 2017-03, Vol.180 (1), p.12057</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933</citedby><cites>FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2563838942?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25732,27903,27904,36991,44569</link.rule.ids></links><search><creatorcontrib>Purnomo, H D</creatorcontrib><creatorcontrib>Hartomo, K D</creatorcontrib><creatorcontrib>Prasetyo, S Y J</creatorcontrib><title>Artificial Neural Network for Monthly Rainfall Rate Prediction</title><title>IOP conference series. Materials Science and Engineering</title><addtitle>IOP Conf. Ser.: Mater. Sci. Eng</addtitle><description>Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Forecasting</subject><subject>Mathematical models</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Rain</subject><subject>Rainfall</subject><issn>1757-8981</issn><issn>1757-899X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqFkE1LAzEQhoMoWKt_QRa8eFk3s0k3yUUopX5Aq-IHeAvpJsHUdbNmt0j_vakrFUHwNMPMM-_Ag9Ax4DPAnGfARizlQjxnwHEGGYYcj9gOGmwXu9uewz46aNslxgWjFA_Q-Th0zrrSqSq5MavwVboPH14T60My93X3Uq2Te-Vqq6oqNp1J7oLRruycrw_RXhy35ui7DtHTxfRxcpXObi-vJ-NZWlLMupSCWgCF3FLLtYACSMm0IFoRmnOqdUEXjIAtCKGlJpyXihLDlGZiYTgRhAzRSZ_bBP--Mm0nl34V6vhS5qOCcMIFzSNV9FQZfNsGY2UT3JsKawlYblzJjQa5USKjKwmydxUPT_tD55uf5PnD9BcmG20jmv-B_pP_CTOyd5Y</recordid><startdate>20170301</startdate><enddate>20170301</enddate><creator>Purnomo, H D</creator><creator>Hartomo, K D</creator><creator>Prasetyo, S Y J</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20170301</creationdate><title>Artificial Neural Network for Monthly Rainfall Rate Prediction</title><author>Purnomo, H D ; Hartomo, K D ; Prasetyo, S Y J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Forecasting</topic><topic>Mathematical models</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Rain</topic><topic>Rainfall</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Purnomo, H D</creatorcontrib><creatorcontrib>Hartomo, K D</creatorcontrib><creatorcontrib>Prasetyo, S Y J</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><jtitle>IOP conference series. Materials Science and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Purnomo, H D</au><au>Hartomo, K D</au><au>Prasetyo, S Y J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Neural Network for Monthly Rainfall Rate Prediction</atitle><jtitle>IOP conference series. Materials Science and Engineering</jtitle><addtitle>IOP Conf. Ser.: Mater. Sci. Eng</addtitle><date>2017-03-01</date><risdate>2017</risdate><volume>180</volume><issue>1</issue><spage>12057</spage><pages>12057-</pages><issn>1757-8981</issn><eissn>1757-899X</eissn><abstract>Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1757-899X/180/1/012057</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1757-8981
ispartof IOP conference series. Materials Science and Engineering, 2017-03, Vol.180 (1), p.12057
issn 1757-8981
1757-899X
language eng
recordid cdi_proquest_journals_2563838942
source Publicly Available Content Database; Free Full-Text Journals in Chemistry
subjects Accuracy
Artificial neural networks
Forecasting
Mathematical models
Model accuracy
Neural networks
Rain
Rainfall
title Artificial Neural Network for Monthly Rainfall Rate Prediction
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T13%3A03%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20Neural%20Network%20for%20Monthly%20Rainfall%20Rate%20Prediction&rft.jtitle=IOP%20conference%20series.%20Materials%20Science%20and%20Engineering&rft.au=Purnomo,%20H%20D&rft.date=2017-03-01&rft.volume=180&rft.issue=1&rft.spage=12057&rft.pages=12057-&rft.issn=1757-8981&rft.eissn=1757-899X&rft_id=info:doi/10.1088/1757-899X/180/1/012057&rft_dat=%3Cproquest_cross%3E2563838942%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c407t-41ab1412f4f8d91613c7d93da34284dd64b731f6334cd388ca43e7ad79be83933%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2563838942&rft_id=info:pmid/&rfr_iscdi=true