基于IABC-RBF神经网络的地下水埋深预测模型
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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.
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.
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.
引文
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[20]王晓慧,刘雪英,白梅花.引入高斯变异和最速下降算子的人口迁移算法[J].计算机工程与应用,2009,45(20):57-60.WANG Xiao-hui,LIU Xue-ying,BAI Mei-hua.Population migration algorithm with Gaussian mutation and the steepest descent operator[J].Computer Engineering and Applications,2009,45(20):57-60.
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[2]CHINH L V,HIRAMATSU K,HARADA M.Estimation of water levels in a main drainage canal in a flat low-lying agricultural area using artificial neural network models[J].Agricultural Water Management,2009,96(9):1332-1338.
[3]张斌,刘俊民.基于BP神经网络的地下水动态预测[J].水土保持研究,2012,19(5):235-237.ZHANG Bin,LIU Jun-min.Prediction of groundwater dynamics based on the BP neural network[J].Research of Soil and Water Conservation,2012,19(5):235-237.
[4]曹文洁,肖长来,梁秀娟,等.RBF神经网络在地下水动态预测中的应用[J].水利水电技术,2018,49(2):43-48.CAO Wen-jie,XIAO Chang-lai,LIANG Xiu-juan,et al.Application of RBF neural network for groundwater dynatic prediction[J].Water Resources and Hydropower Engineering,2018,49(2):43-48.
[5]曹邦兴.基于蚁群径向基函数网络的地下水预测模型[J].计算机工程与应用,2010,46(2):224-226.CAO Bang-xing.Prediction model of underground water level that combined ant colony algorithms with RBF network[J].Computer Engineering and Applications,2010,46(2):224-226.
[6]李慧,周维博,刘博洋,等.基于粒子群优化算法的RBF神经网络在泾惠渠灌区地下水位埋深预测中的应用[J].水电能源科学,2014,32(8):127-130.LI Hui,ZHOU Wei-bo,LIU Bo-yang,et al.Groundwater depth prediction model in Jinghui irrigation district of RBF neural network based on particle swarm optimization[J].Water Resources and Power,2014,32(8):127-130.
[7]徐强,束龙仓,杨桂莲,等.基于遗传算法优化的小波神经网络在地下水位预测中的应用[J].水文,2010,30(1):27-30.XU Qiang,SHU Long-cang,YANG Gui-lian,et al.Application of optimized wavelet neuralnetwork based on genetic algorithm in groundwater level prediction[J].Journal of China Hydrology,2010,30(1):27-30.
[8]KARABOGA D.An idea based on honey beeswarm for numerical optimization[R].Kayseri:Erciyes University,2005.
[9]KARABOGA D,BASTURK B.On the performance of artificial bee colony(ABC)algorithm[J].Applied Soft Computing,2008(8):687-697.
[10]李文莉,李郁侠,任平安.基于云变异人工蜂群算法的梯级水库群优化调度[J].水力发电学报,2014,33(1):37-42.LI Wen-li,LI Yu-xia,REN Ping-an.Optimaloperatin of cascade reservoirs based on cloud variationartificial bee colony algorithm[J].Journal of Hydroelectric Engineering,2014,33(1):37-42.
[11]钱坤,苏国韶.人工蜂群算法在渠道断面优化设计中的应用[J].水利水电科技进展,2011,31(3):57-60.QIAN Kun,SU Guo-shao.Application of artificial bee colony algorithm to optimizationof channel section[J].Advances in Science and Technology of Water Resources,2011,31(3):57-60.
[12]黄文明,徐双双,邓珍荣,等.改进人工蜂群算法优化RBF神经网络的短时交通流预测[J].计算机工程与科学,2016,38(4):713-719.HUANG Wen-ming,XU Shuang-shuang,DENGZhen-rong,et al.Short-term traffic flow prediction of optimized RBF neural networks based on the modified ABC algorithm[J].Computer Engineering and Science,2016,38(4):713-719.
[13]苏彩红,向娜,陈广义,等.基于人工蜂群算法与BP神经网络的水质评价模型[J].环境工程学报,2012,6(2):699-704.SU Cai-hong,XIANG Na,CHEN Guang-yi,et al.Water quality evaluation model based on artificial bee colony algorithm and BP neural network[J].Chinese Journal of Environmental Engineering,2012,6(2):699-704.
[14]许贤杰,周玲,蒋丹,等.基于人工蜂群算法计及线路故障的配电网网络重构[J].电测与仪表,2014,51(3):33-36.XU Xian-jie,ZHOU Ling,JIANG Dan,et al.Distribution network reconfiguration based on artificial bee colony algorithm and line fault[J].Electrical Measurement and Instrumentation,2014,51(3):33-36.
[15]刘三阳,张平,朱明敏.基于局部搜索的人工蜂群算法[J].控制与决策,2014,29(1):123-128.LIU San-yang,ZHANG Ping,ZHU Ming-min.Artificial bee colony algorithm based on local search[J].Control and Decision,2014,29(1):123-128.
[16]罗钧,李研.具有混沌搜索策略的蜂群优化算法[J].控制与决策,2010,25(12):1913-1916.LUO Jun,LI Yan.Artificial bee colony algoithm with chaotic-search strategy[J].Control and Decision,2010,25(12):1913-1916.
[17]暴励,曾建潮.自适应搜索空间的混沌蜂群算法[J].计算机应用研究,2010,27(4):1330-1334.BAO LI,ZENG Jian-chao.Self-adapting search space chaos-artificial bee colony algorithm[J].Application Research of Computers,2010,27(4):1330-1334.
[18]姜鹏,刘俊民,黄一帆,等.泾惠渠地下水对气候变化和人类活动的响应[J].人民黄河,2014,36(5):45-47.JIANG Peng,LIU Jun-min,HUANG Yi-fan,et al.Groundwater response to climate change and human activities in Jinghuiqu[J].Yellow River,2014,36(5):45-47.
[19]向娜.基于神经网络和人工蜂群算法的水质评价和预测研究[D].广州:华南理工大学,2012.XIANG Na.Water quality evaluation and prediction based on neural network and artificial bee colony algorithm[D].Guangzhou:South China University of Technology,2012.
[20]王晓慧,刘雪英,白梅花.引入高斯变异和最速下降算子的人口迁移算法[J].计算机工程与应用,2009,45(20):57-60.WANG Xiao-hui,LIU Xue-ying,BAI Mei-hua.Population migration algorithm with Gaussian mutation and the steepest descent operator[J].Computer Engineering and Applications,2009,45(20):57-60.
[21]刘运,程家兴,林京.基于高斯变异的人工萤火虫算法在云计算资源调度中的研究[J].计算机应用研究,2015,32(3):834-837.LIU Yun,CHENG Jia-xing,LIN Jing.Study of artificial glowworm algorithm based on Gauss mutation in resource scheduling of cloud computing[J].Application Research of Computers,2015,32(3):834-837.
[22]张京京.渠井结合灌区地下水位动态对变化环境的响应及调控研究[D].杨凌:西北农林科技大学,2016.ZHANG Jing-jing.Groundwater dynamic response and regulation under variational environment in canal-well combined irrigation district[D].Yangling:Northwest A&F University,2016.
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