基于人工神经网络的比水容量模型参数预测模型研究  

Research on the Model Parameter Forecasting of Specific Water Capacity Based on Artificial Neural Networks

作　　者：李浩然 樊贵盛[1] LI Hao-ran;FAN Gui-sheng(College of Hydroscience and Engineering,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学水利科学与工程学院,太原030024

出　　处：《中国农村水利水电》2018年第10期197-201,210,共6页China Rural Water and Hydropower

基　　金：国家自然科学基金资助项目(40671081)

摘　　要：为了探索获取比水容量的简便方式,以山西省黄土高原区农耕土壤为实验对象,进行了土壤水分特征曲线与比水容量的相关试验,拟合得到比水容量模型参数,并配套测定了相关的土壤基本理化参数。在研究分析各个土壤基本理化参数与比水容量模型参数的影响关系的基础上,建立了关于土壤质地、土壤容重、土壤有机质含量、土壤无机盐含量的BP神经网络预报模型。研究表明:以土壤质地、土壤容重、土壤有机质含量、土壤无机盐含量为输入因子的BP神经网络预报模型是可行的,比水容量模型参数实测值与预测值之间的平均相对误差均低于10%,预测效果较好,精度较高。该研究结果为黄土高原地区获取比水容量提供了理论与技术上的支持,同时可促进土壤传输函数理论的发展。In order to explore a simple and convenient method for obtaining specific water capacity,the relevant basic physical and chemical parameters of the soil,the soil water characteristic curve and specific water capacity are tested in the soil of the Loess Plateau in Shanxi Province. The parameters of the specific water capacity model are fitted and measured. Based on an analysis of the relationship between the basic physical and chemical parameters of each soil and the parameters of the water capacity model,a BP neural network prediction model is established on the soil texture,soil bulk density,soil organic matter content,and soil inorganic salt content. The results show that the BP neural network prediction model based on soil texture,soil bulk density,soil organic matter content and soil inorganic salt content are feasible,and the average relative error between the measured and predicted values of the water capacity model parameters is lower than 10%,prediction and accuracy are better. The research results provide theoretical and technical supports for obtaining specific water capacity,and promoting the development of soil transfer function theory.

关 键 词：人工神经网络 比水容量模型参数 Gardner经验模型 土壤基本理化参数 

分 类 号：S152[农业科学—土壤学] TV93[农业科学—农业基础科学]