基于AM-MCMC的RAGA-BP网络在灌区水质评价中的应用
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摘要
BP神经网络模型用于水质进行评价的研究已经很多,然而,传统的BP神经网络无法考虑相邻水质级别临界处的模糊性,评价指标较多时运行速度慢,且由于训练样本少和代表性差,评价结果精度不高。建立了基于AM-MCMC算法的RAGA-BP模型,利用RAGA能够选出最优的BP网络初始结构;AM-MCMC算法模拟足够的代表性好的样本为BP网络训练所需,用于灌区的水质评价。实例研究表明,与传统的BP网络相比,基于AM-MCMC的RAGA-BP网络收敛速度提高约20%,评价结果与实际水质比较更为客观、合理。基于AM-MCMC的RAGA-BP模型能考虑相邻水质级别临界处的模糊性,克服训练样本少的缺点生成足够的代表性好的样本,快速有效地对灌区水质进行评价。此外,基于AM-MCMC的RAGA-BP模型还可用于洪灾损失评价、地震灾害评价及其他评价问题,具有广泛的实用性。
Back Propagation Artificial Neural Net is widely used in evaluation of water quality,but it can not determine the fuzziness between adjacent grades of water quality and the convergence velocity and accuracy of estimation are low for lack of training samples.So the BP ANN based on Real Coded Accelerating Genetic Algorithm and Markov Chain Monte Carlo which based on Adaptive Metropolis was built and used to evaluation water quality.RAGA was used to optimize topology,initialize weights and bias of BP;AM-MCMC was adopted to produce enough simulated samples for training BP net and to determine fuzziness between adjacent grades of water quality.Adaptive Metropolis method was taken as a sampling method to improve sampling efficiency of MCMC.Results showed that RAGA-BP based on AM-MCMC can improve convergence velocity by 20%,and the evaluation results of RAGA-BP are more objective,reasonable than that of single indicator method.The model proposed in the paper,considering the fuzziness of boundary between adjacent grades,overcoming the fault of lack of training samples,can rapidly evaluate water quality for irrigation area,the RAGA-BP based on AM-MCMC can be used to evaluate the loss of flood and earthquake.
Back Propagation Artificial Neural Net is widely used in evaluation of water quality,but it can not determine the fuzziness between adjacent grades of water quality and the convergence velocity and accuracy of estimation are low for lack of training samples.So the BP ANN based on Real Coded Accelerating Genetic Algorithm and Markov Chain Monte Carlo which based on Adaptive Metropolis was built and used to evaluation water quality.RAGA was used to optimize topology,initialize weights and bias of BP;AM-MCMC was adopted to produce enough simulated samples for training BP net and to determine fuzziness between adjacent grades of water quality.Adaptive Metropolis method was taken as a sampling method to improve sampling efficiency of MCMC.Results showed that RAGA-BP based on AM-MCMC can improve convergence velocity by 20%,and the evaluation results of RAGA-BP are more objective,reasonable than that of single indicator method.The model proposed in the paper,considering the fuzziness of boundary between adjacent grades,overcoming the fault of lack of training samples,can rapidly evaluate water quality for irrigation area,the RAGA-BP based on AM-MCMC can be used to evaluate the loss of flood and earthquake.
引文
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[2]WALSKI T M.Consumers water quality index[J].Journal of Environment Engineering Division,1974,100(EE3):593-601.
[3]沃飞,陈效民,吴华山,等.灰色聚类法对太湖地区农村地下水水质的评价[J].安全与环境学报,2006,6(4):38-41.
[4]王洪梅,卢文喜,辛光,等.灰色聚类法在地表水水质评价中的应用[J].节水灌溉,2007,(5):20-22.
[5]潘峰,付强,梁川.模糊综合评价在水环境质量综合评价中的应用研究[J].环境工程,2002,20(2):58-61.
[6]管延海,李强,柴成繁.模糊数学方法在天津市地下水水质评价中的应用[J].地下水,2008,30(2):27-30.
[7]庞发虎,张乃群,李玉英,等.标准差权重模糊评价法对南水北调中线水源区水质的评价[J].西北农林科技大学学报:自然科学版,2008,36(2):229-234.
[8]马细霞,贺晓菊,赵道全,等.B-P网络隐含层对水质评价结果的影响分析[J].水电能源科学,2002,9(3):16-18.
[9]黄胜伟,董曼玲.自适应变步长BP神经网络在水质评价中的应用[J].水利学报,2002,10(10):119-123.
[10]HUSKEN M.Structure optimization of neural net-works for evolutionary design optimization[J].Soft computing a fusion of Foundations,Methodologies and Applications,2005,9(1):21-28.
[11]陈兴,程吉林,刘芳.BP神经网络用于水质评价的参数确定[J].水利与建筑工程学报,2007,5(1):12-15.
[12]迟道才.灰色神经网络组合模型(GNN)在涝灾预测中的应用[J].沈阳农业大学学报,2008,39(1):118-120.
[13]ELKAMEL A,BOUHAMRA W.Measurement and prediction of ozone level around a heavily industrialized area:a neuralnetwork approach[J].Advances in Environmental Research,2001,(5):47-59.
[14]王艳琼,白秀琴.基于BP神经网络模型的水质评价及预测[J].武汉工业学院学报,2007,26(1):64-67.
[15]邢贞相,付强.两种实用的涝灾损失频率分析方法[J].系统工程理论与实践,2006,26(2):127-132.
[16]METROPOLIS N,ROSENBLUTH A W,ROSENBLUTH M N,et al.Equations of state calculations by fast computingmachines[J].Journal of Chemical Physics,1953,21:1087-1091.
[17]HASTINGS W K.Monte Carlo sampling methods using markov chains and their applications[J].Biomerika,1970,57:97-109.
[18]陈希儒,郑忠国.现代数学手册.随机数学卷[M].武汉:华中科技大学出版社,2000:498-506.
[19]HARRIO H,SAKSMAN E,TAMMINEN J.An adaptive Metropolis algorithm[J].Bernoulli,2001,7(2):223-242.
[20]GELMAN A.Inference from iterative simulation using multiple sequences[J].Statistics Science,1992,7(4):457-511.
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