Prediction of Climate Variable using Multiple Linear Regression

The change in global temperatures, recent past three years natural disasters, rising sea levels, reducing Polar Regions can be causing the problem of understanding and predicting these climate phenomena. Prediction is a prime importance and they can be run and simulated as computer simulations to predict climate variables temperature, precipitation, rainfall and etc. The state of Kerala saw the worst flood of the century in August 2018, beginning on 15 August 2018. Severe floods affected the south Indian state of Kerala, due to unusually high rainfall during the monsoon season. It was the worst flooding in Kerala in nearly a century. Over 483 people died, and 15 are missing. At least a million people were evacuated. The agricultural country called India in which 60% of the people depending upon the agriculture. Rain fall prediction is the most important task for predicting early prediction of rainfall May helps to peasant's as well as for the people because most of the people in India can be depends upon the agriculture. This paper explains about multiple linear regression technique for the rainfall estimation or prediction. It can helps to farmers for taking appropriate decisions on crop yielding. As usually at the same time there may be a scope to analyze the occurrence of floods or droughts. The multiple linear regression analysis methodology applied on the dataset collected over six years of Nellore district from Andhra Pradesh state. The experiment and our multiple linear regression methodology exploit the appropriate results for the rain fall than simple linear regression methodology.