Hybrid deep learning framework integrating CNNs, transformers, and adaptive optimization for accurate suspended...

Area of Science:

• Environmental Science
• Hydrology
• Data Science

Background:

• Accurate suspended sediment concentration (SSC) prediction is vital for effective reservoir operation, water resource management, and river ecosystem health.
• Forecasting SSC is challenging due to the inherent nonlinearity, nonstationarity, and complex interactions in sediment transport processes.

Purpose of the Study:

• To develop an integrated forecasting framework for robust suspended sediment concentration prediction.
• To enhance the accuracy and generalizability of SSC forecasting models in complex hydrological environments.

Main Methods:

• Variational Mode Decomposition (VMD) for time series decomposition and scale separation.
• Improved Sparrow Search Algorithm (ISSA) for parameter calibration and ensemble weighting.
• Multigene Genetic Programming (MGGP) for feature selection.
• Hybrid Convolution-Transformer Network (CTN) for modeling temporal dependencies.

Main Results:

• The integrated framework achieved high predictive accuracy, with a Nash-Sutcliffe efficiency of 0.9535 on an independent test set.
• The method effectively decomposed SSC time series and selected informative components, reducing redundancy.
• Optimized ensemble weighting improved the overall predictive performance of the forecasting model.

Conclusions:

• The proposed integrated framework offers a robust and generalizable approach for suspended sediment concentration forecasting.
• This method addresses the challenges of nonlinearity and nonstationarity in sediment transport modeling.
• The findings support improved decision-making in water resource management and ecological restoration.