RAINFALL PREDICTION APP USING MACHINE LEARING

RAINFALL PREDICTION APP USING MACHINE LEARING

B. Ashwath, D. Bharath Reddy, B. Yuvaraj , Pardhu Charan, Mr. T. Raghavendra Gupta

Hyderabad Institute of  Technology And Management

ABSTRACT
One of the most significant and difficult responsibilities in the modern world is rainfall forecasting. In general, weather and precipitation are very complex, nonlinear processes that require sophisticated computer modelling and simulation to accurately anticipate. For informationintensive applications, it will be important to swiftly find and analyze complex patterns and needs as well as to learn from fresh data. One answer to this is an artificial neural network. Such nonlinear systems' behavior can be predicted using ANN. Throughout the past 25 years, the majority of researchers in this area have employed ANN with success. The ANN methodology is more appropriate for predicting rainfall than conventional statistical and numerical techniques. Support vector machines (SVM) are a data-classification technique that assignsnew data components to one of the labelled categories. It makes theassumption that the relevant data contains some potential target values.

KEYWORDS
Artificial Neural Network, Rainfall Prediction System, Support Vector Classification (SVC), Support Vector Regression (SVR), multi-layer perceptron (MLP), Neural Networks Model.