Predicting hydrological droughts using ERA 5 reanalysis data and wavelet-based soft computing techniques

KATİPOĞLU O. M.

Environmental Earth Sciences, cilt.82, sa.24, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 82 Sayı: 24
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s12665-023-11280-9
  • Dergi Adı: Environmental Earth Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Drought prediction, Machine learning, Reconnaissance Drought Index (RDI), Standardized Runoff Index (SRI), Wavelet transform
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

The effective forecasting of droughts is the basic step of drought risk management, which consists of drought preparedness, mitigation, and early warning phases. This study used historical values of meteorological and hydrological drought indices to predict ERA 5 reanalysis data based on future hydrological droughts with a 1-month lead-time in Diyarbakır, in the Tigris basin. Regression tree (RT), boosted tree (BT), bagged tree (BAT), support vector machines (SVM), and adaptive neuro-fuzzy inference system (ANFIS) models and hybrid wavelet-machine learning models created by combining these models with discrete wavelet transform were analyzed to predict Standardized Runoff Index (SRI) based hydrological droughts. To construct the models and determine the temporal dependencies and relationships between variables, autocorrelation functions (ACF), partial autocorrelation functions (PACF), and correlation matrices were used. In addition, statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and, coefficients of determination (R 2), and the Taylor diagram were used to evaluate the success of the established models. The study has provided evidence that the application of wavelet transform can significantly improve the efficacy of hydrological drought prediction. The ANFIS model with Train RMSE: 0.65, MAE: 0.47, R 2: 0.49; Test RMSE: 0.84, MAE: 0.64, R 2: 0.39, which combines SRI (T − 1) and SRI (T) inputs, is considered the best stand-alone machine learning model for hydrological drought prediction. Furthermore, the most successful prediction performance is achieved by combining the SRI (T − 3), SRI (T − 2), SRI (T − 1), and SRI (T) inputs using the wavelet transform and BAT algorithm with Train RMSE: 0.59, MAE: 0.45, R 2: 0.59; Test RMSE: 0.74, MAE: 0.60, R 2: 0.48. Accordingly, it was concluded that hydrological droughts could be predicted using only previous hydrological droughts. Therefore, the study's outputs can be used to effectively plan water resources for decision-makers and various institutions in managing drought risk.