针对新疆渭干河-库车河三角洲绿洲土壤盐分动态监测中存在的方法问题，首先用灰色关联度模型分析影响形成土壤盐渍化的各因子，并确定其与土壤盐分之间的关联度，然后将人工智能计算技术引入土壤盐分的预测中，经过多次调整网络结构和参数，建立了预测表层土壤盐分的BP神经网络模型和RBF神经网络模型。结果表明：以潜在蒸散量、地下水埋深、地下水矿化度、土壤电导率、总溶解固体、pH值、坡度和土地利用类型8个因素为输入因子，以土壤含盐量为输出因子的BP网络模型和RBF网络模型可有效模拟土壤盐分与其影响因子之间的内在复杂关系，并且有较高的精度。BP网络模型预测误差略低于RBF神经网络。本研究可为分析和预测土壤盐渍化动态规律提供一种有效可行的新途径，是对传统土壤盐分动态研究的补充。

Aiming at the problem indynamic monitoring the soil salinity in the oasis of the Ugan Kuqa River Delta in Xinjiang, we analyzed the factorsaffectingthe soil salinization withthe gray-correlation-degree model, and determinedthe degree of the association between the factors and the soil salinity. The artificial intelligence technology was utilized in the soil salinity prediction. After several adjustments on the network structure and parameters, we established a BP neural network model and a RBF neural network model to predict surface soil salinity. The results showed that the BP network model and RBF network model, takingthe evapotranspiration, groundwater depth, groundwater mineralization, soil conductivity, total dissolved salts, pH value, slope and land-use type as input factors andsoil salinity as output factor, couldeffectively simulate soil salinity and its impact to the inherent complexity of the relationship between the factors.The prediction error of the BP network model was less than that of the RBF. The present study could provide an effective and viable new way for analyzing and predicting the soil salinization. Itcould be a complement to the traditional dynamicsmonitoringon soil salinity.