Improved Moth-Swarm Algorithm to predict transient storage model parameters in natural streams.

Area of Science:

• Environmental science
• Hydrology
• Computational modeling

Background:

• The Transient Storage Model (TSM) is widely used for simulating solute transport in rivers.
• Accurate estimation of TSM parameters is crucial for hydraulic and environmental management.
• Existing methods may struggle with the complexity of solute transport processes.

Purpose of the Study:

• To develop and validate an improved Moth-Swarm Algorithm (IMSA) for predicting TSM parameters.
• To assess the performance of IMSA against benchmark functions and existing algorithms.
• To enhance the accuracy of solute transport simulations in natural streams.

Main Methods:

• An improved high-level Moth-Swarm Algorithm (IMSA) was utilized.
• Model performance was initially validated using benchmark functions.
• A dataset of 58 hydraulic and geometric measurements was randomly split for model derivation and verification.
• Results were compared using Root Mean Square Error (RMSE) and Coefficient of Correlation (CC).

Main Results:

• IMSA demonstrated successful performance on benchmark functions.
• The algorithm accurately predicted TSM parameters using the validation dataset.
• IMSA outperformed previously proposed algorithms in predicting TSM parameters.
• High accuracy was achieved despite the inherent complexity of dispersion processes.

Conclusions:

• The improved Moth-Swarm Algorithm (IMSA) provides an accurate method for estimating TSM parameters.
• IMSA offers a robust solution for solute transport modeling in natural streams.
• This approach enhances the reliability of environmental and hydraulic simulations.