Enhanced hydrological drought prediction in the Gediz Basin: integrating meteorological drought via hybrid wavelet-machine learning-random oversampling models using

Abstract

ABSTRACT In study, meteorological drought ​​was used to estimate the potential hydrological drought that may occur in the Gediz Basin of Turkey. For this purpose, the most effective stream flow gauging station was determined by looking at the correlation values ​​between the meteorological data obtained from the Uşak meteorological station. SPEI values ​​for meteorological drought and SRI values ​​for hydrological drought are calculated for 3-, 6-, 9-, and 12-month periods. Correlation matrices were created between meteorological drought inputs from SPEI(t) to SPEI(t-12) and SRI(t) for use in hydrological drought models for 3-, 6-, 9- and 12-month periods. ML models were developed considering correlation matrices and it was seen that ML model results were not sufficient. For this reason, W-ML models were developed by applying DWT and Optuna hyperparameter analysis. It has been observed that the performance of W-ML models increases. Random oversampling (ROS), which has never been used in drought modeling, was then applied to W-ML models. W-ML-ROS model obtained an R2 value of 0.999 for testing set in 12-month period. Similarly, R2 values ​​for SRI3, SRI6 and SRI9 were obtained as 0.893, 0.851, and 0.940, respectively. Results showed that W-ML-ROS hybrid models can be used to predict hydrological drought from meteorological drought.