A novel fuzzy random forest model for meteorological drought classification and prediction in ungauged catchments

Abstract

This paper presents a new tree-based model, namely Fuzzy Random Forest (FRF), for one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) classification and prediction with a noteworthy application in ungauged catchments. The proposed FRF model uses global SPEI dataset as the meteorological drought quantifier and applies a fuzzy inference system to extract fuzzified and crisp SPEI values for an ungauged catchment. The evolved crisp series is then transformed into the polynomial label vector of extremely wet, wet, near normal, dry, and extremely dry categories. In the end, the state-of-the-art random forest algorithm was used to classify and predict the label vector using the lagged SPEI series of the selected global grid points. To demonstrate the development and verification process of the FRF model, the global SPEI-6 values for the period of 1961–2015 were retrieved from four global grid points around the Central Antalya Basin, Turkey. The new model was trained and validated using 70% and 30% of the data sets, respectively. The performance of the new model was examined in terms of total accuracy, misclassification, and Kappa statistics and cross-validated with the fuzzy decision tree model developed as the benchmark in this study. The results showed the promising performance of the FRF for drought classification and prediction with outstanding efficiency for extremely wet and dry events classification. According to the Kappa statistic, the proposed FRF model is 25% more accurate than the benchmark FDT model.