Rainfall Prediction using Regression Model

Artificial intelligence is been widely used in all the applications and weather forecasting is not an exception. When it comes to weather forecasting, rainfall prediction is one of the most widely used research areas as numerous lives and property damages occur due to this. Intense rainfall has abundant impacts on society and on our daily life from cultivation to disaster measures. Previous rainfall prediction models that are widely used, makes use of many the complicated blend of mathematical instruments which was insufficient to get a higher classification rate. In this project, we propose new novel methods for predicting monthly rainfall using linear regressionanalysis. Rainfall predictions are made by collecting quantitative data about the current state of the atmosphere. Numerous machine learning algorithms can learn complex mappings from inputs to outputs, based solely on samples and require limited. Accurate prediction of rainfall is a difficult task due to the dynamic nature of the atmosphere. To predict the future’s rainfall condition, the variation in the conditions in past years must be utilized. We have proposed the use of linear regressions by making use of various parameters such as temperature, humidity,and wind. The proposed model tends to forecast rainfall based on the previous records of a particular geographic area, therefore, this prediction will prove to be much reliable. The performance of the model is more accurate when compared with traditional rainfall prediction systems