Rainfall Prediction Using Machine Learning
Akash Gupta, Hitesh Kumar Mall, S. Janarthanan
Abstract
Rainfall forecasting is a single of difficult and unpredictable undertakings that has a major influence on human society. Predictions that are accurate and timely can help to avert human and financial loss. This study contains a series of experiments that include the utilisation of basic machine learning techniques to build Weather forecasting models that estimate whether it will rain in major cities tomorrow based on the day’s meteorological data. This comparative research looks at three aspects of modelling inputs, modelling methodologies, and preprocessing procedures. The findings demonstrate how different machine learning systems perform on a range of assessment parameters, as well as their capacity to forecast rainfall using weather data analysis. Agriculture is crucial to India’s survival. The importance of rainfall in agriculture cannot be overstated. Rainfall forecasting has been a key problem in recent years. Individuals can be more aware of the weather and make more educated judgments by predicting rainfall.