In India, agriculture is the most important factor for survival of human being. For agriculture, the most important thing is water. i.e., rainfall. Nowadays rainfall prediction is a major problem. Predicting the amount of rainfall gives alertness to farmers by knowing early so that they can protect their crops and properties from rain. There are more techniques to predict the rainfall. The ML algorithms are best suited for prediction of rainfall. Here are some of the major ML algorithms used rapidly which are Auto regressive integrated moving average Model (ARIMA), Artificial neural network (ANN), Support Vector Machine, Logistic regression, and Self organizing map. And there are two models commonly used to predict periodic rainfall such as Linear and Non-linear models. ARIMA Model is the first used model. Although using ANN (Artificial neural network) the prediction of rain can easily completed by using Cascade NN, Layer recurrent network, or Back propagation NN. Artificial NN is similar as Biological neural networks
This study presents a set of experiments that involve the use of common machine learning techniques to create models that can predict whether it will rain tomorrow or not based on the weather data for that day in major cities in Australia.
Nowadays, rainfall is considered to be one of the most liable factor for most of the significant things in the world. In India, agriculture is one of the most important factor in deciding the economy of the country and agriculture is totally dependent on the rainfall. Apart from agriculture, rainfall is also more important in coastal areas around the world by getting to know the rainfall is very much necessary to protect their life’s from the floods and heavy rainfall. In some of the areas which are having drought, to establish an rainfall harvester, proper prediction of rainfall is necessary.
We can observe that XGBoost, CatBoost and Random Forest performed better compared to other models. However, if speed is an important thing to consider, we can stick with Random Forest instead of XGBoost or CatBoost.