{"title":"Rainfall Forecasting Based on Surface Data of Chennai Region\nUsing Artificial Neural Networks","authors":"","doi":"10.46243/jst.2020.v5.i6.pp26-36","DOIUrl":null,"url":null,"abstract":"In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural\nNetwork using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been\ncollected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was\norganized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative\nHumidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been\nused both for training and for testing the data. The developed system gives more accuracy of 94.8197% when the\ntraining data set is 55% and the testing data set is 45% with least Mean Squared Error (MSE) value 0.012437.","PeriodicalId":23534,"journal":{"name":"Volume 5, Issue 4","volume":"58 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5, Issue 4","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46243/jst.2020.v5.i6.pp26-36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural
Network using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been
collected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was
organized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative
Humidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been
used both for training and for testing the data. The developed system gives more accuracy of 94.8197% when the
training data set is 55% and the testing data set is 45% with least Mean Squared Error (MSE) value 0.012437.