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

Saubhagya S., Tilakaratne C., MƏMMƏDOV M., Lakraj P.

Engineering Proceedings, vol.39, no.1, 2023 (Scopus) identifier

  • Nəşrin Növü: Article / Article
  • Cild: 39 Say: 1
  • Nəşr tarixi: 2023
  • Doi nömrəsi: 10.3390/engproc2023039006
  • jurnalın adı: Engineering Proceedings
  • Jurnalın baxıldığı indekslər: Scopus
  • Açar sözlər: cost-sensitive, data mining, deep learning, ensemble, imbalance learning, spatial kriging
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Bəli

Qısa məlumat

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.