Through analysis of the pumping test data, which were affected by errors, an aquifer was investigated in order to present a new method for estimating the aquifer parameters. Based on the self-paced learning method in the machine learning field, a self-paced learning method based on the differential evolution algorithm was established and applied to the determination of aquifer parameters. This method was compared with other methods under errors of different levels. The numerical experimental results show that under errors of different levels, the differences between the values estimated with this method and traditional methods are small, and there are minor differences between the simulation data and original data. The results of the self-paced learning method, which was used for estimation of aquifer parameters based on pumping test data, are effective and reliable. The established algorithm is highly stable and it is seldom affected by data errors.%通过分析受到误差影响的抽水试验数据进行含水层估计,为含水层参数估计提供方法支持.以机器学习领域的自步学习方法为基础,构造了基于差分进化算法优化的自步学习方法,并将其应用到含水层参数确定中;在不同误差水平下,与其他方法进行对比试验.结果表明,在不同误差水平下,估计参数值与传统估计值之间以及仿真数据与原始数据之间均保持较小差异;基于抽水试验数据估计含水层参数的自步学习方法估计结果有效可靠,算法对误差的稳定容错性强.
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