关键词: 随机漂移粒子群算法, 稀疏网格, 根区水质量模型（RZWQM）, 替代模型

Abstract: Root zone water quality model is widely used to describe the influence of soil hydrological cycle on crop growth, and guide the agriculture production management through model simulation. However, once calibration needs long time, it is a difficult task to find a suitable model parameters in an acceptable time; besides, traditional trial-and-error calibration method depends largely on user’s experience and expertise, and needs to try more times to get satisfactory simulation results. A sparse grid method is employed to construct surrogate model of RZWQM and random drift particle swarm optimization algorithm is applied in parameters optimization of surrogate model. The optimized parameters are used in practical simulation of RZWQM. The surrogate model has high precision and calibration speed, which greatly saves computational cost in parameters optimization. Five years of yield, drain flow, and [NO-3]-N loss data from a subsurface-
drained corn-soybean field in Iowa are employed in empirical analysis of sparse grid surrogate model with random drift particle swarm optimization. The results show that proposed method can greatly improve the efficiency of model parameter optimization and save manpower, and can get better performance of RZWQM model through evaluation index with PBIAS, NSE and RSR.

Key words: random drift particle swarm optimization algorithm, sparse grid, Root Zone Water Quality Model（RZWQM）, surrogate model