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基于贝叶斯神经网络的农田分区灌溉需水量模拟

朱爱华 戴光鑫

朱爱华,戴光鑫.基于贝叶斯神经网络的农田分区灌溉需水量模拟[J].农业工程,2022,12(7):78-83. doi: 10.19998/j.cnki.2095-1795.2022.07.015
引用本文: 朱爱华,戴光鑫.基于贝叶斯神经网络的农田分区灌溉需水量模拟[J].农业工程,2022,12(7):78-83. doi: 10.19998/j.cnki.2095-1795.2022.07.015
ZHU Aihua,DAI Guangxin.Simulation of water demand for farmland subarea irrigation based on Bayesian neural network[J].Agricultural Engineering,2022,12(7):78-83. doi: 10.19998/j.cnki.2095-1795.2022.07.015
Citation: ZHU Aihua,DAI Guangxin.Simulation of water demand for farmland subarea irrigation based on Bayesian neural network[J].Agricultural Engineering,2022,12(7):78-83. doi: 10.19998/j.cnki.2095-1795.2022.07.015

基于贝叶斯神经网络的农田分区灌溉需水量模拟

doi: 10.19998/j.cnki.2095-1795.2022.07.015
详细信息
    作者简介:

    朱爱华,学士,高级工程师,主要从事农田水利研究E-mail:jifang46149598@163.com

  • 中图分类号: S275

Simulation of Water Demand for Farmland Subarea Irrigation Based on Bayesian Neural Network

  • 摘要:

    针对农田分区灌溉需水量模拟过程中普遍存在的求解过程易陷入局部最小化、出现过度拟合,以及过度依赖历史用水数据,导致最终模拟结果存在显著误差的问题,研究基于贝叶斯神经网络的农田分区灌溉需水量模拟分析方法。以前一周需水量、年内月需水量占比、日内温度上限值及日降雨量为指标,通过聚类分析获取指标数据均值,对农田分区灌溉历史用水的样本数据进行聚类分析。构建贝叶斯神经网络模型,将指标数据均值输入模型,根据BP神经网络原理与贝叶斯规则训练指标数据,然后输出农田分区灌溉需水量模拟结果。试验结果显示数据聚类结果中数据间关联度高于95%,数据拟合效果较好,模拟需水量时具有更高的精度与稳定性。

     

  • 图 1  贝叶斯神经网络隐藏层结构

    Figure 1.  Hidden layer structure of Bayesian neural network

    图 2  贝叶斯神经网络训练流程

    Figure 2.  Training process of Bayesian neural network

    图 3  基于贝叶斯神经网络的数据拟合结果

    Figure 3.  Data fitting results based on Bayesian neural network

    图 4  对比方法数据拟合结果

    Figure 4.  Data fitting results of comparison methods

    图 5  不同农田区域灌溉需水量模拟对比

    Figure 5.  Simulation comparison of irrigation water demand in different farmland areas

    图 6  隐含层节点数量对于模拟结果的影响

    Figure 6.  Influence on simulation results of hidden layer nodes number

    表  1  需水量模拟结果与实际监测结果对比

    Table  1.   Comparison of water demand simulation results and actual monitoring results

    日期需水量监测值/m3需水量模拟值/m3
    基于BP神经网络的方法基于DSSAT模型的方法本文方法
    2020年7月16日1175348118493311895121179212
    2020年7月17日1224679120953412084161224079
    2020年7月18日1201622121220012170561215846
    2020年7月19日1169951117153411714691170268
    2020年7月20日1172316118027511807191173498
    2020年7月21日1236301122651312254741235495
    2020年7月22日1122917111492511398151120687
    2020年7月23日721920726874730005726541
    2020年7月24日690349735870736841700042
    2020年7月25日614468648412641587618714
    2020年7月26日1284574134887412058791290475
    2020年7月27日98722310254991026987981274
    2020年7月28日1003856105862410498561007486
    2020年7月29日1247372118956512795211239577
    2020年7月30日594371578784625410584265
    下载: 导出CSV

    表  2  数据聚类结果关联度

    Table  2.   Correlation degree of data clustering results 单位:%

    指标区域A区域B区域C区域D
    Xn-197.9195.7497.9896.46
    Xn-298.2696.0596.8297.21
    Xn-396.0498.2397.0095.84
    Xn-498.7396.4796.5995.63
    Xn-595.0797.3697.8696.84
    Xn-696.9596.8895.8196.28
    Xn-797.2298.2996.4297.06
    Tn95.9996.0797.5098.44
    Rn96.5496.8596.2396.51
    Sn97.3098.0695.8997.92
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-31
  • 修回日期:  2022-02-24
  • 出版日期:  2022-07-20

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