VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Abstract

Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

“…The evaluation of results, measured using the Root Mean Square Error (RMSE), clearly indicated that VMD-XGBoost outperformed DWT-XGBoost. Ali Danandeh Mehr [20] proposed an evolutionary explicit model, Variable Mode Decomposition Genetic Programming (VMD-GP) for SPEI prediction in ungauged catchment areas. GP is a regression technique.…”

A New Multi-Objective Genetic Programming Model for Meteorological Drought Forecasting

“…Overall, the relevant literature proved that drought forecasting is a challenging task due to highly stochastic patterns existing in their representative indices. While a classic time-series analysis method or an ad hoc ML technique fails to accurately identify underlying patterns, particularly for long-term forecasts, the hybrid/ensemble ML models were preferred to do the task, indicating that there is always room for additional improvement [18,19]. Our review of the hydrological applications of ML revealed that most of the available studies have focused on increasing the forecasting accuracy, which is given less or even no attention to the rising complexity due to hybridization via adding an external optimization technique or assembling different ML techniques.…”

“…This is clearly due to the high nonlinear characteristics of the SPI series that make its predictability hard [37]. The relevant literature showed that data preprocessing techniques such as wavelet or variational mode decomposition may increase the accuracy of vanilla models [18,33]. However, their inclusion in the modeling process increases the solution's complexity markedly.…”