Wavelet-Based precipitation preprocessing for improved drought Forecasting: A Machine learning approach using tunable Q-factor wavelet transform and maximal overlap discrete wavelet transform

Abstract

Drought forecasting plays a crucial role in mitigating the severe agricultural and social consequences caused by droughts. The fluctuating nature of droughts makes it difficult to develop an effective drought forecasting model without preprocessing the input data. This paper proposes a novel approach that introduces the tunable Q-factor wavelet transform (TQWT) with the maximal overlap discrete wavelet transform (MODWT) based Fejér–Korovkin, Coiflet, and Daubechies filters in the decomposition of precipitation data for the extended lead time forecasting of the standardized precipitation evapotranspiration index (SPEI). The decomposed datasets have been coupled with Matern Gaussian process regression (MGPR), exponential Gaussian process regression (EGPR), linear support vector machine (LSVM), and coarse Gaussian support vector machine (CGSVM), and formed hybrid models to forecast SPEI-12 and SPEI-18 for several lead times (i.e., 6, 12, 18, and 24 months). Results of the study represent that the wavelet-based hybrid models are capable of predicting SPEI-12 and SPEI-18 effectively for different lead times with promising results. Both TQWT and MODWT coupled with MGPR yielded reasonable performances for the lead time of 6 months in all stations. However, for the higher lead times, TQWT coupled with MGPR outperformed other hybrid models. The results of the TQWT-MGPR for SPEI-12 are more effective than SPEI-18 in different lead times. The study highlights that preprocessing of precipitation data using TQWT is a promising direction for drought forecasting, and the findings obtained from drought forecasting can be utilized in the areas of water and agricultural resource management to effectively mitigate and alleviate the potential impacts of future droughts.