Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

Abstract

Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems.

Performance Comparison of Various Machine Learning Models for Rainfall Prediction Using Wind Information

“…Among these models, XGBoost demonstrated superior performance, attributed to its capability to handle complex relationships between variables. Another approach, an ensemble of K-stars (EK-stars), was proposed for next-day rainfall prediction using meteorological data from Australia [5]. This study introduced a probability-based aggregating (pagging) approach, surpassing the original K-star algorithm and other recent studies in terms of classification accuracy.…”