T. Anandharajan, G. A. Hariharan, K. K. Vignajeth, R. Jijendiran, Kushmita
{"title":"使用人工智能进行天气监测","authors":"T. Anandharajan, G. A. Hariharan, K. K. Vignajeth, R. Jijendiran, Kushmita","doi":"10.1109/CINE.2016.26","DOIUrl":null,"url":null,"abstract":"Weather forecasting is rather a statistical measure than a binary decision. We intend to develop an intelligent weather predicting module since this has become a necessary tool. This tool considers measures such as maximum temperature, minimum temperature and rainfall for a sampled period of days and are analyzed. An intelligent prediction based on the available data is accomplished using machine learning techniques. The analysis and prediction is based on linear regression which predicts the next day's weather with good accuracy. An accuracy of more than 90% is obtained, based on the data set. Recent studies have reflected that machine learning techniques achieved better performance than traditional statistical methods. Machine learning, a branch of artificial intelligence has been proved to be a robust method in predicting and analyzing a given data set. The module plays a vital role in agricultural, industrial and logistical fields where the weather forecast is an important criterion.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"Weather Monitoring Using Artificial Intelligence\",\"authors\":\"T. Anandharajan, G. A. Hariharan, K. K. Vignajeth, R. Jijendiran, Kushmita\",\"doi\":\"10.1109/CINE.2016.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Weather forecasting is rather a statistical measure than a binary decision. We intend to develop an intelligent weather predicting module since this has become a necessary tool. This tool considers measures such as maximum temperature, minimum temperature and rainfall for a sampled period of days and are analyzed. An intelligent prediction based on the available data is accomplished using machine learning techniques. The analysis and prediction is based on linear regression which predicts the next day's weather with good accuracy. An accuracy of more than 90% is obtained, based on the data set. Recent studies have reflected that machine learning techniques achieved better performance than traditional statistical methods. Machine learning, a branch of artificial intelligence has been proved to be a robust method in predicting and analyzing a given data set. The module plays a vital role in agricultural, industrial and logistical fields where the weather forecast is an important criterion.\",\"PeriodicalId\":142174,\"journal\":{\"name\":\"2016 2nd International Conference on Computational Intelligence and Networks (CINE)\",\"volume\":\"150 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Computational Intelligence and Networks (CINE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINE.2016.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINE.2016.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Weather forecasting is rather a statistical measure than a binary decision. We intend to develop an intelligent weather predicting module since this has become a necessary tool. This tool considers measures such as maximum temperature, minimum temperature and rainfall for a sampled period of days and are analyzed. An intelligent prediction based on the available data is accomplished using machine learning techniques. The analysis and prediction is based on linear regression which predicts the next day's weather with good accuracy. An accuracy of more than 90% is obtained, based on the data set. Recent studies have reflected that machine learning techniques achieved better performance than traditional statistical methods. Machine learning, a branch of artificial intelligence has been proved to be a robust method in predicting and analyzing a given data set. The module plays a vital role in agricultural, industrial and logistical fields where the weather forecast is an important criterion.