Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960-2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t + 1 output variable by using t, t - 1, t - 2, and t - 3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE = 0.9345, OI = 0.9438, R-2 = 0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE = 0.9528, OI = 0.9559, R-2 = 0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE = 0.9398, OI = 0.9483, R-2 = 0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.

Automated summary

In this study, short-term meteorological droughts were predicted using hybrid machine learning models and pre-processing techniques. The results showed that the hybrid models with pre-processing techniques performed better than standalone models.