Rainfall Prediction using XGB Model with the Australian Dataset

Surendra Reddy Vinta; Radhika Peeriga

doi:10.4108/ew.5386

Authors

Surendra Reddy Vinta Vellore Institute of Technology University
Radhika Peeriga Marri Laxman Reddy Institute of Technology and Management

DOI:

https://doi.org/10.4108/ew.5386

Keywords:

Rainfall prediction, Machine Learning, Randomforest, Logistic regression, Gaussian Naive Bayes, K-nearest neighbors, Support Vector classifier, XGBoost, Accuracy, Cross-validation, Performance metrics, Australian rainfall dataset, Data preprocessing, Feature selection, Missing value imputation, Practical applications

Abstract

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.

Downloads

Download data is not yet available.

References

Mandhare, A., & Tijare, S. (2019). Comparative study of rainfall prediction using machine learning techniques. In Proceedings of the International Conference on Computing Methodologies and Communication (ICCMC) (pp. 12-15). IEEE.

Han, D., Kim, M., & Kim, S. (2018). Rainfall prediction using machine learning techniques. In Proceedings of the International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-4). IEEE.

Liang, S., Liu, Y., Zhang, W., & Huang, Q. (2018). A rainfall prediction method based on machine learning ensemble. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC) (pp. 1894-1899). IEEE.

Hou, D., Liu, Y., Xu, J., & Xie, W. (2017). Rainfall prediction using machine learning algorithms with hyperparameter optimization. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC) (pp. 798-803). IEEE.

Mazumdar, R., & Deb, D. (2019). Hybrid ARIMA-ANN model for accurate rainfall prediction. In Proceedings of the International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 84-87). IEEE.

Li, S., Li, J., & Li, W. (2018). Rainfall prediction using deep learning. In Proceedings of the IEEE International Conference on Big Data (Big Data) (pp. 5100-5107). IEEE.

Li, Y., Liu, J., Liu, X., & Zhu, J. (2019). Rainfall prediction using long short-term memory network with attention mechanism. IEEE Access, 7, 14205-14214.

Li, Y., Zhao, C., & Huang, L. (2020). Rainfall prediction using a novel hybrid deep learning model. IEEE Access, 8, 186385-186394.

Lin, J., Zhang, X., & Wang, Y. (2018). A new rainfall prediction model based on gradient boosting decision tree.

Chen, J., Li, W., Li, M., & Li, Y. (2018). Rainfall prediction based on improved extreme learning machine algorithm. IEEE Access, 6, 77780-77788.

Ray, R., Gupta, S., & Ray, S. (2019). Machine learning approach to rainfall prediction. In Proceedings of the International Conference on Communication, Devices and Computing (ICCDC) (pp. 27-31). IEEE.

Jain, D., & Jain, V. K. (2018). Rainfall prediction using machine learning algorithms: A comparative study. In Proceedings of the International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Sharma, A., & Garg, N. (2018). Rainfall prediction using machine learning techniques. In Proceedings of the International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 432-435). IEEE.

Ma, X., Zhang, L., & Yin, J. (2020). Rainfall prediction based on long short-term memory and support vector regression. IEEE Access, 8, 104081-104092. DOI: https://doi.org/10.1109/ACCESS.2020.2994655

Sun, J., & Zheng, C. (2020). A rainfall prediction model based on attention mechanism and improved gradient boosting decision tree. IEEE Access, 8, 193599-193609.

Park, J., & Kim, S. (2018). Rainfall prediction using a convolutional neural network with dilated convolution. In Proceedings of the International Conference on Control, Automation and Systems (ICCAS) (pp. 301-304). IEEE.

Qi, Y., & Li, H. (2019). Rainfall prediction based on stacked autoencoder and LSTM network. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC) (pp. 1031-1035). IEEE.

Garg, N., & Sharma, A. (2018). Rainfall prediction using machine learning algorithms: A review. International Journal of Computer Science and Information Security, 16(2), 54.