摘要
根据黄河河套灌区多年的水文、气象和地下水资料 ,应用不同的 A NN— BP网络模型对灌区年、月地下水埋深的变化进行了模拟 ,预测了黄河河套灌区节水工程实施后未来灌区年平均地下水位下降的情况 ,从预测结果可以看出 ,河套灌区节水工程实施后灌区平均地下水位预计下降 0 .5 m左右。此外对 BP模型的结构设计中隐含层数、隐含单元数和学习速率的合理确定作了具体分析 ,提出了学习速率取值范围 lr =0 .0 1～ 0 .1,为河套灌区节水工程改造规划、设计和管理决策及 BP模型的应用提供了参考依据。
This paper applies a three and four layer Artificial Neural Network model (BP) based on long term regional groundwater, hydrological and climate data, for simulating dynamic movement of annual and monthly groundwater depth of the Hetao Irrigation District and forecasting the trend of change after reconstructing a water saving engineering project. The average depth will decrease 0.51 m compared with present average depth in 2010. At the same time, it discusses the influence of different BP hidden layers, hidden units number and learning rates on the fitting errors, training efficiency, etc. The range of learning rates was presented as lr=0.01~0.1. The results showed that the BP network could be applied for regional groundwater forecast, and has a higher calculating precision. This will provide an important foundation in the planning, design, and management of the water saving reconstruction project in the large scale irrigation district, and for the application of BP model in groundwater hydrological prediction.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2003年第1期59-62,共4页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然基金资助项目 (50 1 790 1 3)
内蒙古自治区水利厅重点科研联合资助项目
