An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

Author(s): Pundra Chandra Shaker Reddy*, Alladi Sureshbabu

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 10 , Issue 4 , 2020

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Graphical Abstract:


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.

Keywords: Linear regression, hadoop, Map-Reduce, climate data, temperature, rainfall.

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Article Details

Year: 2020
Page: [473 - 483]
Pages: 11
DOI: 10.2174/2210327910666191218124350
Price: $25

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