Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques

Drought is a slowly developing phenomenon and possibly influences a wide domain. Drought index is one of the ways in monitoring and surveying drought, hence Palmer drought severity index (PDSI) has been used as a valid and operational model. In this study the Discrete Wavelet Transform (DWT) tool is incorporated with Fuzzy, k-Nearest Neighbour (kNN) and Support Vector Machine (SVM) modelling tools to improve forecasting accuracy and extend lead time. DWT is further used to decompose original PDSI data into wavelets (sub-series) which, in turn, are used as inputs into the Fuzzy, kNN, and SVM models for the development of a new model in forecasting PDSI for longer lead times from 1 to 12 months. DWT combined with Fuzzy, kNN and SVM models are termed as W-Fuzzy, W-kNN and W-SVM models. The predictive models are implemented in the Marmara region of Turkey. The accuracy of combined hybrid W-Fuzzy, W-kNN and W-SVM models are compared with stand-alone Fuzzy, kNN and SVM models by using Mean Square Error (MSE), Coefficient of Efficiency (CE) and Coefficient of Determination (R²) as performance indicators. The results of this study reveal that developed hybrid W-Fuzzy, W-kNN, and W-SVM models performed very well up to lead time of 6 months. Furthermore, combined W-Fuzzy, W-kNN and W-SVM models are performed better than stand-alone Fuzzy, kNN and SVM models. However, the prediction performance of W-Fuzzy model is slightly better than those of W-kNN and W-SVM models for all lead time predictions in terms of performance indicator criteria, MSE, CE and R².