基于IABC-RBF神经网络的地下水埋深预测模型

doi:10.3785/j.issn.1008-973X.2019.07.011

浙江大学学报(工学版)

2019, Vol. 53

Issue (7): 1323-1330 DOI: 10.3785/j.issn.1008-973X.2019.07.011

自动化技术、计算机技术

基于IABC-RBF神经网络的地下水埋深预测模型

邵光成1(

),章坤1,王志宇1,王小军2,3,卢佳1

1. 河海大学农业工程学院，江苏南京 210098
2. 南京水利科学研究院水文水资源与水利工程科学国家重点实验室，江苏南京 210029
3. 水利部应对气候变化研究中心，江苏南京 210029

Groundwater depth prediction model based on IABC-RBF neural network

Guang-cheng SHAO1(

),Kun ZHANG1,Zhi-yu WANG1,Xiao-jun WANG2,3,Jia LU1

1. College of Agricultural Engineering, Hohai University, Nanjing 210098, China
2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
3. Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China

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摘要：

为了验证基于改进人工蜂群算法的径向基函数（RBF）神经网络模型在地下水埋深预测中的可行性和优越性，在基本人工蜂群算法中引入高斯变异算子，优化初始蜜源位置，设计建立基于改进人工蜂群算法的RBF神经网络模型（IABC-RBF）. 通过输入泾惠渠灌区的年降雨量、年渠首引水量、年田间灌溉用水量、年地下水开采量和前一年的地下水埋深共5个相关影响因子的数据，对地下水埋深进行预测，与实测的地下水埋深数据进行比较，误差很小. 与RBF神经网络模型和基于基本人工蜂群算法训练的RBF神经网络模型（ABC-RBF）的预测结果进行比较，结果表明，基于改进人工蜂群算法的RBF神经网络模型收敛速度更快，预测结果误差最小，精度最高.

关键词： 人工蜂群算法; 径向基函数神经网络; 高斯变异; 泾惠渠灌区; 地下水埋深; 预测

Abstract:

The Gaussian mutation operator was introduced into the basic artificial bee colony algorithm, and the initial nectar location was optimized in order to verify the feasibility and superiority of radial basis function (RBF) neural network model based on improved artificial bee colony algorithm in groundwater depth prediction. A RBF neural network model was designed based on improved artificial bee colony algorithm (IABC-RBF). The groundwater depth was predicted by inputting the annual rainfall, the amount of annual water intake in the first ditch, the amount of annual field irrigating, the annual groundwater exploitation and the groundwater depth in the previous year of Jinghui irrigation district. The error is very small compared with the measured groundwater depth data. The model was compared with the RBF neural network model and the RBF neural network model based on the basic artificial bee colony algorithm (ABC-RBF). Results show that the RBF neural network model based on the improved artificial bee colony algorithm has faster convergence speed, the least error of the prediction result and the highest accuracy.

Key words: artificial bee colony algorithm radial basis function neural network Gaussian mutation Jinghui irrigation district groundwater depth prediction

收稿日期: 2018-05-17 出版日期: 2019-06-25

CLC:

P 332

作者简介: 邵光成（1975—），男，教授，从事农业水资源高效利用研究. orcid.org/0000-0001-6353-362X. E-mail: sgcln@126.com

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引用本文:

邵光成,章坤,王志宇,王小军,卢佳. 基于IABC-RBF神经网络的地下水埋深预测模型[J]. 浙江大学学报(工学版), 2019, 53(7): 1323-1330.

Guang-cheng SHAO,Kun ZHANG,Zhi-yu WANG,Xiao-jun WANG,Jia LU. Groundwater depth prediction model based on IABC-RBF neural network. Journal of ZheJiang University (Engineering Science), 2019, 53(7): 1323-1330.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.07.011 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I7/1323

图 1 各影响因素与地下水埋深动态关系图

图 2 径向基函数（RBF）神经网络模型结构示意图

图 3 改进人工蜂群算法优化过程图

图 4 RBF神经网络模型结构示意图

表 1 RBF神经网络模型参数训练结果

图 5 IABC-RBF神经网络模型预测值与实测值对比图

表 2 3种模型预测结果的对比

图 6 3种模型预测结果的平均相对误差对比

表 3 预测结果的平均相对误差和方差对比

图 7 每次迭代后的最佳值

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