Predicting Future Rainfall with Various Machine Learning Models

Abstract

This research presents a machine learning technique that improves the prediction of the annual rainfall total. Predicting the amount and timing of precipitation in a given region is known as rainfall prediction. The global community is quite concerned about the accuracy of rain forecasts. People know this is the cause of floods and other natural disasters every year. Any number of industries might feel the effects of inclement weather, including farming, building, power generating, and tourism. Precipitation forecasting is one of the most challenging and uncertain undertakings due to the far-reaching effects it has on human society. The only way to reduce needless pain and financial losses is with timely and accurate predictions. Using historical meteorological data for a single day in major Australian cities, this paper describes a series of experiments that build models that can anticipate the possibility of rain tomorrow using cutting-edge machine learning techniques. This comparative study will look at inputs, methodologies, and pre-processing strategies in great detail. Using a variety of measures that measure the algorithms' capacity to understand weather data and predict the likelihood of precipitation, the results reveal how well these machine learning algorithms performed. Machine learning has proven to be extremely useful at predicting when it will rain, which is currently the most fundamental need, At the moment, it's quite tough to say with any certainty when it will rain. In our endeavors to forecast precipitation, we have employed a plethora of methods, such as Decision Tree Algorithm, Linear Regression, Support Vector Regression, Random Forest Regressor, and Random Forest Classifier. In agricultural contexts, the effective rainfall is a key factor in deciding the rate of crop growth. Forecasting rainfall using machine learning can improve water resource planning, agricultural production, and water usage prediction.