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Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model

机译：基于优化算法的多步参数估计方法在萨克拉曼多模型中的应用

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摘要

The Sacramento model is widely utilized in hydrological forecast, of which the accuracy and performance are primarily determined by the model parameters, indicating the key role of parameter estimation. This paper presents a multi-step parameter estimation method, which divides the parameter estimation of Sacramento model into three steps and realizes optimization step by step. We firstly use the immune clonal selection algorithm (ICSA) to solve the non-liner objective function of parameter estimation, and compare the parameter calibration result of ideal artificial data with Shuffled Complex Evolution (SCE-UA), Parallel Genetic Algorithm (PGA), and Serial Master-slaver Swarms Shuffling Evolution Algorithm Based on Particle Swarms Optimization (SMSE-PSO). The comparison result shows that ICSA has the best convergence, efficiency and precision. Then we apply ICSA to the parameter estimation of single-step and multi-step Sacramento model and simulate 32 floods based on application examples of Dongyang and Tantou river basins for validation. It is clearly shown that the results of multi-step method based on ICSA show higher accuracy and 100% qualified rate, indicating its higher precision and reliability, which has great potential to improve Sacramento model and hydrological forecast.
机译：萨克拉曼多模型在水文预报中得到了广泛的应用，其准确性和性能主要取决于模型参数，这说明了参数估计的关键作用。本文提出了一种多步骤的参数估计方法，该方法将萨克拉曼多模型的参数估计分为三个步骤，并逐步实现优化。我们首先使用免疫克隆选择算法（ICSA）求解参数估计的非线性目标函数，然后将理想的人工数据的参数校准结果与改组复杂进化（SCE-UA），并行遗传算法（PGA），和基于粒子群优化（SMSE-PSO）的串行主从群混合改组进化算法。比较结果表明，ICSA具有最佳的收敛性，效率和精度。然后将ICSA应用于萨克拉曼多单步模型和多步模型的参数估计，并基于东阳河和坦头河流域的应用实例对32次洪水进行模拟以进行验证。可以清楚地看出，基于ICSA的多步法结果具有较高的准确度和100％的合格率，表明其较高的精确度和可靠性，对于改进萨克拉曼多模型和水文预报具有很大的潜力。

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