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Volume 39
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Proceeding Paper

An Application of Ensemble Spatiotemporal Data Mining Techniques for Rainfall Forecasting †

by
Shanthi Saubhagya
1,*,
Chandima Tilakaratne
1,
Musa Mammadov
2 and
Pemantha Lakraj
1
1
Department of Statistics, University of Colombo, Colombo P.O. Box 1490, Sri Lanka
2
School of Info Technology, Faculty of Science, Engineering and Built Environment, Geelong Waurn Ponds Campus, Deakin University, Geelong P.O. Box 423, Australia
*
Author to whom correspondence should be addressed.
†
Presented at the 9th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 12–14 July 2023.
Eng. Proc. 2023, 39(1), 6; https://doi.org/10.3390/engproc2023039006
Published: 27 June 2023
(This article belongs to the Proceedings of The 9th International Conference on Time Series and Forecasting)
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Abstract

The study proposes an ensemble spatiotemporal methodology for short-term rainfall forecasting using several data mining techniques. Initially, Spatial Kriging and CNN methods were employed to generate two spatial predictor variables. The three days prior values of these two predictors and of other selected weather-related variables were fed into six cost-sensitive classification models, SVM, Naïve Bayes, MLP, LSTM, Logistic Regression, and Random Forest, to forecast rainfall occurrence. The outperformed models, SVM, Logistic Regression, Random Forest, and LSTM, were extracted to apply Synthetic Minority Oversampling Technique to further address the class imbalance problem. The Random Forest method showed the highest test accuracy of 0.87 and the highest precision, recall and an F1-score of 0.88.
Keywords: deep learning; spatial kriging; ensemble; cost-sensitive; data mining; imbalance learning deep learning; spatial kriging; ensemble; cost-sensitive; data mining; imbalance learning

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MDPI and ACS Style

Saubhagya, S.; Tilakaratne, C.; Mammadov, M.; Lakraj, P. An Application of Ensemble Spatiotemporal Data Mining Techniques for Rainfall Forecasting. Eng. Proc. 2023, 39, 6. https://doi.org/10.3390/engproc2023039006

AMA Style

Saubhagya S, Tilakaratne C, Mammadov M, Lakraj P. An Application of Ensemble Spatiotemporal Data Mining Techniques for Rainfall Forecasting. Engineering Proceedings. 2023; 39(1):6. https://doi.org/10.3390/engproc2023039006

Chicago/Turabian Style

Saubhagya, Shanthi, Chandima Tilakaratne, Musa Mammadov, and Pemantha Lakraj. 2023. "An Application of Ensemble Spatiotemporal Data Mining Techniques for Rainfall Forecasting" Engineering Proceedings 39, no. 1: 6. https://doi.org/10.3390/engproc2023039006

APA Style

Saubhagya, S., Tilakaratne, C., Mammadov, M., & Lakraj, P. (2023). An Application of Ensemble Spatiotemporal Data Mining Techniques for Rainfall Forecasting. Engineering Proceedings, 39(1), 6. https://doi.org/10.3390/engproc2023039006

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