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

Abstract

ABSTRACT In this study, meteorological droughts were used to estimate the potential hydrological drought that may occur in the Gediz Basin of Turkey. For this purpose, the most effective streamflow gauging station was determined by looking at the correlation values between the meteorological data obtained from the Uşak meteorological station. Standardized precipitation evapotranspiration index (SPEI) values for meteorological drought and standardized runoff index (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. Machine learning (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 discrete wavelet transform and Optuna hyperparameter analysis. It has been observed that the performance of W-ML models increases. 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 the 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.