hduser

The study of rainfall drifts and its estimation is very important for a country like India whose economy is totally dependent on the availability of water resources basically from rainfall for farming. A generic knowledge of the trend in rainfall of a particular area has a great importance because of economic suggestions of the rain sensitive operations and since it plays the important role of any agricultural and non-agricultural activity. Throughout For entire India, no prominent inclination was ascertained for yearly, seasonally or monthly rainfall. Analyzing the past rainfall data and predicting the future precipitation will help the nation to manage water resources. The work in this paper focuses on the analysis of imbalanced rainfall data for the past 115 years to forecast the rainfall in forthcoming years. The results are correlated to predict the rainfall using MapReduce based Hadoop framework and R programming. Additionally, the imbalance within the data sets is addressed by an enhanced over sampling technique: Minority Majority Mix mean Over_Sampling Technique (MMMmOT). The analysis is carried out by time series model and likelihood is based on simple exponential smoothing model. The results achieved noticeably estimate the supremacy of the proposed MMMmOT technique over R package for improved estimation. The forecasted values present a minimal error compared to actual values. The current research work will help the respective organizations and experts working on uncertain rainfall problem in India to provide a pre-remedial insight.