Aims & Background: India is a country which has exemplary climate circumstances
comprising of different seasons and topographical conditions like high temperatures, cold atmosphere,
and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact
weather prediction is a challenging task. Majority people of the country depend on agriculture.
Farmers require climate information to decide the planting. Weather prediction turns into an orientation
in farming sector to deciding the start of the planting season and furthermore quality and
amount of their harvesting. One of the variables are influencing agriculture is rainfall.
Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that
helpful to people who live in regions which are inclined natural calamities such as floods and it
helps agriculturists for decision making in their crop and water management using big data analytics
which produces high in terms of profit and production for farmers. In this project, we proposed
an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM)
with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian
Metrological Department, Hyderabad) in 1901 to 2002 period.
Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with
better accuracy compared with other existing models.
Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by
anticipating long-term seasonal rainfall.