Rainfall Prediction using XGB Model with the Australian Dataset.

Vinta, Surendra Reddy; Peeriga, Radhika

Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.

RAINFALL; MACHINE learning; RANDOM forest algorithms; LOGISTIC regression analysis; K-nearest neighbor classification; SUPPORT vector machines; ELECTRONIC data processing; FEATURE selection

EAI Endorsed Transactions on the Energy Web, 2024, Vol 11, Issue 1, p1

2032-944X

Academic Journal

10.4108/ew.5386