Analyzing Machine Learning Models for Forecasting Precipitation in Australia

Abstract

In the 21st century, predicting when it will rain is an intriguing but challenging task. Climate and precipitation representations are frequently extremely complicated, non-linear, and inconsistent, need highly skilled, specialized mathematical modeling and training. The rise in rainfall-related flood tragedies in recent decades has made weather forecasting an increasingly important area of study. Most of the time, the researcher tried to find a linear relationship between the necessary data and the meteorological data that was already accessible. This work uses conventional machine learning algorithms to give a thorough analysis and prediction model for rainfall in Australia. Enhancing the precision and dependability of rainfall forecasts is the aim of this research. The dataset for the study contains historical meteorological data, such as temperature, humidity, wind speed, and air pressure, from multiple locations of Australia. Using classic machine learning techniques like Random Forest (RF) and Naive Bays closest neighbors, baseline models are created. Model evaluation is a meticulous procedure that contrasts the accuracy, precision, and memory of models. The primary meteorological factors that influence the variability of rainfall are identified using the feature importance analysis. The interpretability of the models is also investigated in the study in order to offer insightful information about the decision-making procedures. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of machines learning techniques, including Random Forest model (RF), K nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes (NB), and Logistic Regression (LR). This research intends to mitigate the high risks of floods induced by natural disasters by utilizing state-of-the-art models. The results show that random forests have high accuracy (0.859) for predicting rainfall.