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生产建设项目土壤流失量的预测直接关系到建设项目的水土保持分析、评价和防治措施体系的布局,目前常用的预测方法因其局限性、不合理性以及精度差等问题往往难以实现准确预测。将人工神经网络的BP算法引入到土壤流失量预测中,将降雨侵蚀力、土壤可蚀性、坡长、坡度、水土保持措施作为影响土壤流失量的主要因子,并以17个生产建设项目水土保持监测实例作为学习样本和检测样本,建立了基于神经网络方法的土壤流失量预测模型。预测结果表明,该模型拟合和预测精度高,具有很强的应用价值,能够满足工程应用需要。
Prediction of soil loss in production and construction projects is directly related to the layout of soil and water conservation analysis, evaluation and prevention and control measures of construction projects. Currently, the commonly used prediction methods are often difficult to predict accurately due to their limitations, unreasonableness and poor accuracy. The artificial neural network (BP) algorithm is introduced into the prediction of soil loss, and rainfall erosivity, soil erodibility, slope length, slope and water and soil conservation measures are taken as the main factors affecting the soil loss. Based on the 17 production and construction projects, Maintaining monitoring examples as learning samples and testing samples, a prediction model of soil loss based on neural network was established. The prediction results show that the model has high fitting and prediction precision, and has strong application value, which can meet the needs of engineering application.