Calibrating RZWQM2 model using quantum-behaved particle swarm optimization algorithm

The RZWQM2 model employs a number of parameters and coefficients requiring calibration and validation to accurately predict how interacting environmental conditions and management approaches influence crop development, water flow and pollutant levels in an agricultural production system. Given these requirements, an auto-calibration tool capable of optimizing RZWQM2 parameters would greatly enhance model efficiency over manual trial-and-error calibration. With a single parameter controlling convergence speed, the easy-to-implement quantum-behaved particle swarm optimization (QPSO) algorithm has performed well in deriving solutions to a wide range of optimization problems. Five years (2005-2009) of yield, drain flow, and NO3--N loss data from a subsurface-drained corn-soybean field in Iowa were employed in assessing the feasibility of using such an algorithm as an adjunct to RZWQM2. Using the QPSO algorithm, RZWQM2 parameters were calibrated using single- and multiple-objective functions. The algorithm-enhanced and standard models' performances in simulating yield, drainage, and NO3-N loss were compared on the basis of the percent bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the ratio of root mean squared error to standard error (RSR). With the QPSO algorithm the model calibration by individual parameter yielded PBIAS, NSE and RSR values of ±10%, over 0.90, and less than 0.44, respectively, while for the collectively calibrated model values were ±12%, over 0.83, and under 0.41, comparable to the performance achieved through manual calibration. This study suggests that the models calibrated with the QPSO algorithm operated in a satisfactory manner, and this method can be used successfully in estimating parameters in RZWQM2.