Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China

Area of Science:

• Hydrology and Water Resource Management
• Time Series Analysis
• Entropy Theory Applications

Background:

• Streamflow forecasting is essential for various water management applications like reservoir operation and flood control.
• Monthly streamflow data exhibit complex seasonal and periodic patterns requiring sophisticated forecasting methods.
• Entropy-based methods offer a novel approach to analyzing and predicting hydrological time series.

Purpose of the Study:

• To develop and evaluate streamflow forecasting models using different entropy principles.
• To assess the performance of maximum Burg entropy (BESA), maximum configurational entropy (CESA), and minimum relative entropy (RESA) models.
• To compare the forecasting accuracy of these models for monthly streamflow at five hydrological stations in northwest China.

Main Methods:

• Construction of forecasting models based on maximum Burg entropy, maximum configurational entropy, and minimum relative entropy.
• Utilizing average relative error (RE), root mean square error (RMSE), correlation coefficient (R), and determination coefficient (DC) for performance evaluation.
• Application of entropy spectral analysis techniques to monthly streamflow time series data.

Main Results:

• The RESA model demonstrated the highest overall forecasting accuracy.
• The CESA model also exhibited high forecasting accuracy, performing better than BESA in general.
• The BESA model showed superior performance during low-flow periods, while RESA and CESA excelled during flood seasons.

Conclusions:

• Entropy spectral analysis methods provide effective tools for monthly streamflow forecasting.
• The RESA and CESA models are recommended for general streamflow prediction, with BESA being suitable for low-flow conditions.
• Further research should explore the applicability of these entropy-based methods to other river systems in China.