A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms

Abstract

The difficult challenge of predicting rainfall is brought on by the daily observations of erratic rainfall patterns and climatic fluctuations. Predicting when the rain will fall can help avoid floods and even aid in crop growth in agriculture. Timely and precise predictions can prevent loss of life and assets. The ability to forecast the amount of rainfall requires an understanding of weather-related elements such as pressure, humidity, wind speed, latitude, longitude, and precipitable water with varying x and y-axis parameters. The research in this study involves using fundamental machine learning techniques to create weather forecasting models that use the day's meteorological data to predict whether or not it will rain tomorrow. By utilizing previously identified trends from historical meteorological data, machine learning helps forecast rainfall. We are using a classification model in our supervised data model, and the techniques utilized to forecast the amount of rain include random forest, KNN, decision tree, and logistic regression. Using machine learning algorithms to examine past weather data and find patterns that can be applied to forecast future rainfall patterns is the suggested technique for rainfall prediction. A more accurate weather forecast is made possible by all of the aforementioned factors. We will handle the information that aids in eliminating erroneous and incomplete data. As a component of data preparation, normalization helps to improve feature approximation by adjusting the range of independent variables. Model training is carried out following data preparation, during which data is divided into training and test sets. The test set aids in prediction-making, while the training set serves as the foundation for model training.