An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during...

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Hydrology

Background:

• Accurate water quality modeling is crucial for managing surface runoff.
• InfoWorks ICM is a widely used tool for hydrological modeling.
• Calibration of multiple water quality parameters presents a complex challenge.

Purpose of the Study:

• To develop an automatic calibration framework for water quality parameters in surface runoff modeling.
• To integrate a genetic algorithm (GA) for optimizing calibration processes.
• To compare the effectiveness of multi-objective versus single-objective calibration.

Main Methods:

• Construction of an automatic calibration framework using a genetic algorithm (GA).
• Consideration of multiple water quality parameters: total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).
• Utilized four objective functions (NSE, R², PEP, PBIAS) as fitness evaluators for the GA.

Main Results:

• The multi-objective calibration framework was successfully applied to a case study in Fuzhou, China.
• Multi-objective calibration resulted in lower comprehensive indexes for TSS, COD, TN, and TP compared to single-objective calibration.
• Multi-objective calibration demonstrated increased efficiency by reducing the number of iterations required to reach optimal values.

Conclusions:

• The multi-objective function GA provides a more balanced and efficient approach to water quality modeling calibration.
• The developed framework offers a reliable basis for model uncertainty evaluation and subsequent applications.
• The framework is adaptable to other modeling programs by adjusting objective functions and weights for enhanced accuracy and efficiency.