Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input.

Area of Science:

• Hydrology
• Water Resource Management
• Data Science

Background:

• Accurate groundwater depth forecasting is vital for human survival and sustainable water resource management, especially in arid and semi-arid regions.
• Existing forecasting methods often lack the precision required for effective groundwater management.

Purpose of the Study:

• To develop and validate a novel hybrid groundwater depth forecasting model.
• To enhance prediction accuracy by incorporating optimal input selection and advanced decomposition techniques.

Main Methods:

• The study employed variational mode decomposition (VMD) to decompose groundwater depth series into different frequency subseries.
• Configurational entropy spectral analyses (CESA) was utilized for forecasting, with optimal input series selected via cross-correlation analysis and Shannon entropy.
• The hybrid model's performance was assessed using data from four monitoring wells in Xi'an, China.

Main Results:

• The hybrid model demonstrated superior prediction performance compared to standalone CESA and autoregressive models.
• Evaluation metrics including average relative error (RE), root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliffe coefficient (NSE) confirmed the hybrid model's accuracy.
• The model successfully forecasted groundwater depth, highlighting its practical applicability.

Conclusions:

• The developed hybrid model offers a significant advancement in groundwater depth forecasting accuracy.
• This approach provides a reliable tool for sustainable groundwater management in water-scarce regions.
• The integration of VMD and CESA with optimal input selection represents a promising direction for hydrological forecasting.