Parameter Optimization for the Xin'anjiang Hydrological Model
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In this document, we conduct an in-depth exploration of the parameter optimization algorithm for the Xin'anjiang hydrological model and examine its application in simulating rainfall-runoff processes. The algorithm typically employs optimization techniques such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to minimize objective functions like Nash-Sutcliffe Efficiency (NSE) or Root Mean Square Error (RMSE). Furthermore, we investigate the algorithm's adaptability to other hydrological models and its role in enhancing model accuracy and predictive capabilities through systematic parameter sensitivity analysis. Notably, this research has broad applications not only in hydrology but also in meteorology and environmental science, demonstrating its cross-disciplinary potential. Thus, we recognize the algorithm's significant value and understand why it remains a focal point in contemporary scientific research.
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