Abstract
Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.
Highlights
Irrigation is the prime need for agricultural crop production in Bangladesh and most of irrigation depends upon the rain
The experiments were performed to evaluate the accuracy of rainfall prediction using multiple linear regressions
We have selected a method for rainfall prediction after analysis of Rajshahi rainfall dataset which is derived by some data mining techniques like firstly apply correlation analysis regression analysis
Summary
Irrigation is the prime need for agricultural crop production in Bangladesh and most of irrigation depends upon the rain. The empirical approach is based on analysis of historical data of the rainfall and its relationship to a variety of atmospheric and oceanic variables over different parts of the world. The most widely used empirical approaches, which are used for climate prediction, are regression, artificial neural network, fuzzy logic and group method of data handling. The Dynamical approach is implemented by using numerical rainfall forecasting method [1]. Regression models are often used for estimating the future events or values based on the previous values and events. Regression is a statistical empirical technique and is widely used in business, the social and behavioral sciences, the biological sciences, climate prediction, and many other areas. Non-parametric methodologies such as projection pursuit, additive models, multivariate adaptive regression etc. have been applied to estimation and prediction problems [2]
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