{"title":"Quasi Analysis of Rainfall Prediction during Floods using Machine Learning","authors":"K. Bhargavi, G. Suma","doi":"10.1145/3390525.3390535","DOIUrl":null,"url":null,"abstract":"Floods are the most common natural disasters and researchers turned their spotlight on prediction of rainfall for rescuing lives of the people before or after its arrival. The intensity of flood majorly relies on heavy rainfall. If the rainfall is predicted well in advance it will be useful for taking precautionary measures. In this paper, predictive analysis is carried out using both classification and regression models. The prediction analysis is evaluated with the feature rain Tomorrow feature in the dataset. The computation analysis shows that prediction using Random Forest classifier and nth Polynomial regression gives exactness for assessment.","PeriodicalId":201179,"journal":{"name":"Proceedings of the 2020 8th International Conference on Communications and Broadband Networking","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 8th International Conference on Communications and Broadband Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3390525.3390535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Floods are the most common natural disasters and researchers turned their spotlight on prediction of rainfall for rescuing lives of the people before or after its arrival. The intensity of flood majorly relies on heavy rainfall. If the rainfall is predicted well in advance it will be useful for taking precautionary measures. In this paper, predictive analysis is carried out using both classification and regression models. The prediction analysis is evaluated with the feature rain Tomorrow feature in the dataset. The computation analysis shows that prediction using Random Forest classifier and nth Polynomial regression gives exactness for assessment.