Drought classification using gradient boosting decision tree

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models were developed based on multi-station prediction scenarios in which observed (reanalyzed) data from nearby stations (grid points) were used to predict drought conditions in a target location. To tackle the rare occurrence of extreme dry/wet conditions, the drought series at the target location was categorized into three classes of wet, normal, and dry events. The new models were trained and validated using the first 70% and last 30% of the datasets, respectively. The results demonstrated the promising performance of GBT for meteorological drought classification. It provides better performance than DT and GP in Ankara; however, GP predictions for Antalya were more accurate in the testing period. The results also exhibited that the proposed GP model with a scaled sigmoid function at root can effortlessly classify and predict the number of dry, normal, and wet events in both case studies.

Automated summary

This study compared the classification and prediction capabilities of decision tree, genetic programming, and gradient boosting decision tree techniques for forecasting precipitation and evaporation indexes. GBT showed promising performance in Ankara, while GP was more accurate in central Antalya. The GP model with a scaled sigmoid function demonstrated ease in classifying and predicting different events in the case studies.