仿生算法优化BP神经网络在降雨空间插值中的应用
|
摘要
人工神经网络能够充分挖掘已知样本中的规律,从而对未观测数据进行预测,可应用于降雨量空间插值计算中。在BP神经网络进行降雨空间插值的基础上,引入遗传、粒子群和蚁群3种仿生算法对BP神经网络初始权值和阈值进行优化,将优化后的BP神经网络应用于三峡区间流域年、月和日3个时间尺度的降雨空间插值中。结果表明:仿生算法对BP神经网络初始权值和阈值优化求解后,降低了BP神经网络陷入局部最小以及过拟合的风险,在插值过程中表现出较好的稳定性,取得了理想的插值结果。
The artificial neural network can dig out the regulation of the observed data fully to have a prediction to the unobserved data,and can be applied in the spatial interpolation of rainfall. Based on the traditional BP neural network in spatial interpolation of rainfall,this paper introduces the genetic algorithm,particle swarm optimization and ant colony algorithm to optimize the initial weights and thresholds of BP neural network,and are applied in the spatial interpolation of rainfall of year,month and day such three temporal scales in the Three Gorges region. The results showed that after the optimization of initial weights and thresholds of BP neural network by bionic algorithms,it can reduce risk of BP trapping in the partial smallest and the over fitting. There is a better stability in the interpolation process to get the ideal interpolation.
The artificial neural network can dig out the regulation of the observed data fully to have a prediction to the unobserved data,and can be applied in the spatial interpolation of rainfall. Based on the traditional BP neural network in spatial interpolation of rainfall,this paper introduces the genetic algorithm,particle swarm optimization and ant colony algorithm to optimize the initial weights and thresholds of BP neural network,and are applied in the spatial interpolation of rainfall of year,month and day such three temporal scales in the Three Gorges region. The results showed that after the optimization of initial weights and thresholds of BP neural network by bionic algorithms,it can reduce risk of BP trapping in the partial smallest and the over fitting. There is a better stability in the interpolation process to get the ideal interpolation.
引文
[1]张继国,谢平,龚艳冰,等.降雨信息空间插值研究评述与展望[J].水资源与水工程学报,2012,23(1):6-9+13.
[2]张唯,龙亚斐,张剑波,等.融合时序特征的湖北省降水空间分布估算[J].长江流域资源与环境,2014,23(10):1398-1405.
[3]胡广义,张秋文,张勇传.基于BP人工神经网络的分布式降雨量插值估算[J].华中科技大学学报(自然科学版),2009,37(4):107-110.
[4]胡广义,张秋文,张勇传. GA优化的BPNN模型在分布式降雨量插值中的应用[J].武汉大学学报(工学版),2009,42(4):466-469.
[5]莫林,张秋文.人工神经网络在降水量空间插值中的应用研究[J].计算机与数字工程,2007,35(9):9-12+207.
[6]KAJORNRIT J,WONG K W,FUNG C C. An interpretable fuzzy monthly rainfall spatial interpolation system for the construction of aerial rainfall maps[J]. Soft Computing,2014,20(12):4631-4643.
[7]SIVAPRAGASAM C,ARUN V M,GIRIDHAR D. A simple approach for improving spatial interpolation of rainfall using ANN[J]. Meteorology and Atmospheric Physics,2010,109(1-2):1-7.
[8]PIAZZA A D,CONTI F L,NOTO L V,et al. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily,Italy[J]. International Journal of Applied Earth Observations&Geoinformation,2011,13(3):0-408.
[9]刘国东,戴振学,邢冰,等.仿生算法在地下水模型反演中的应用现状与展望[J].水文地质工程地质,2016,43(1):41-49.
[10]杨鑫,任海霞,万芳.水库径流预报的蚁群优化神经网络算法应用研究[J].中国农村水利水电,2013,(12):9-12+18.
[11]卜松,王军,郭力文. ACA-BP网络在冰塞水位预测中的应用[J].人民黄河,2009,31(5):45-47.
[12] KIM S,SINGH V P,SEO Y,et al. Modeling nonlinear monthly evapotranspiration using soft computing and data reconstruction techniques[J]. Water Resources Management,2014,28(1):185-206.
[13] BUYUKYILDIZ M,TEZEL G,YILMAZ V. Estimation of the change in lake water level by artificial intelligence methods[J]. Water Resources Management,2014,28(13):4747-4763.
[14] ASADNIA M,CHUA L H C,QIN X S,et al. Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling[J]. Journal of Hydrologic Engineering,2013,19(7):1320-1329.
[15]AZIZ K,RAI S,RAHMAN A. Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network(GAANN)technique for Australia[J]. Natural Hazards,2015,77(2):805-821.
[16]吴昌广,林德生,周志翔,等.三峡库区降水量的空间插值方法及时空分布[J].长江流域资源与环境,2010,19(7):752-758.
[17]朱蕾,黄敬峰.山区县域尺度降水量空间插值方法比较[J].农业工程学报,2007,23(7):80-85.
[18]王小川,史峰,郁磊. MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
[19]王德明,王莉,张广明.基于遗传BP神经网络的短期风速预测模型[J].浙江大学学报(工学版),2012,46(5):837-841+904.
[20]胡广义.分布式降雨量估算模型与方法研究[D].武汉:华中科技大学,2009.
[21]夏哲兵,刘国东,任玉峰,等.基于遗传算法的模糊综合评价法在地下水质评价中的应用[J].环境卫生工程,2014,22(6):75-77+80.
[22]李祚泳,蔡辉,丁晶. BP网络的学习能力与推广能力之间满足的不确定关系式[J].四川大学学报(工程科学版),2003,35(1):15-19.
[23]覃光华,李祚泳. BP网络过拟合问题研究及应用[J].武汉大学学报(工学版),2006,39(6):55-58.
[24]覃光华,丁晶,陈彬兵.预防过拟合现象的人工神经网络训练策略及其应用[J].长江科学院院报,2002,19(3):59-61.
[25] GONGA-SAHOLIARILIVA N,NEPPEL L,CHEVALLIER P,et al. Geostatistical estimation of daily monsoon precipitation at fine spatial scale:Koshi River Basin[J].Journal of Hydrologic Engineering. 2016,21(9):05016017.
[26]张继国,管耀宗,朱永忠.降雨空间分布的模糊熵聚类分析[J].河海大学学报(自然科学版). 2016,44(4):353-357.
[2]张唯,龙亚斐,张剑波,等.融合时序特征的湖北省降水空间分布估算[J].长江流域资源与环境,2014,23(10):1398-1405.
[3]胡广义,张秋文,张勇传.基于BP人工神经网络的分布式降雨量插值估算[J].华中科技大学学报(自然科学版),2009,37(4):107-110.
[4]胡广义,张秋文,张勇传. GA优化的BPNN模型在分布式降雨量插值中的应用[J].武汉大学学报(工学版),2009,42(4):466-469.
[5]莫林,张秋文.人工神经网络在降水量空间插值中的应用研究[J].计算机与数字工程,2007,35(9):9-12+207.
[6]KAJORNRIT J,WONG K W,FUNG C C. An interpretable fuzzy monthly rainfall spatial interpolation system for the construction of aerial rainfall maps[J]. Soft Computing,2014,20(12):4631-4643.
[7]SIVAPRAGASAM C,ARUN V M,GIRIDHAR D. A simple approach for improving spatial interpolation of rainfall using ANN[J]. Meteorology and Atmospheric Physics,2010,109(1-2):1-7.
[8]PIAZZA A D,CONTI F L,NOTO L V,et al. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily,Italy[J]. International Journal of Applied Earth Observations&Geoinformation,2011,13(3):0-408.
[9]刘国东,戴振学,邢冰,等.仿生算法在地下水模型反演中的应用现状与展望[J].水文地质工程地质,2016,43(1):41-49.
[10]杨鑫,任海霞,万芳.水库径流预报的蚁群优化神经网络算法应用研究[J].中国农村水利水电,2013,(12):9-12+18.
[11]卜松,王军,郭力文. ACA-BP网络在冰塞水位预测中的应用[J].人民黄河,2009,31(5):45-47.
[12] KIM S,SINGH V P,SEO Y,et al. Modeling nonlinear monthly evapotranspiration using soft computing and data reconstruction techniques[J]. Water Resources Management,2014,28(1):185-206.
[13] BUYUKYILDIZ M,TEZEL G,YILMAZ V. Estimation of the change in lake water level by artificial intelligence methods[J]. Water Resources Management,2014,28(13):4747-4763.
[14] ASADNIA M,CHUA L H C,QIN X S,et al. Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling[J]. Journal of Hydrologic Engineering,2013,19(7):1320-1329.
[15]AZIZ K,RAI S,RAHMAN A. Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network(GAANN)technique for Australia[J]. Natural Hazards,2015,77(2):805-821.
[16]吴昌广,林德生,周志翔,等.三峡库区降水量的空间插值方法及时空分布[J].长江流域资源与环境,2010,19(7):752-758.
[17]朱蕾,黄敬峰.山区县域尺度降水量空间插值方法比较[J].农业工程学报,2007,23(7):80-85.
[18]王小川,史峰,郁磊. MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
[19]王德明,王莉,张广明.基于遗传BP神经网络的短期风速预测模型[J].浙江大学学报(工学版),2012,46(5):837-841+904.
[20]胡广义.分布式降雨量估算模型与方法研究[D].武汉:华中科技大学,2009.
[21]夏哲兵,刘国东,任玉峰,等.基于遗传算法的模糊综合评价法在地下水质评价中的应用[J].环境卫生工程,2014,22(6):75-77+80.
[22]李祚泳,蔡辉,丁晶. BP网络的学习能力与推广能力之间满足的不确定关系式[J].四川大学学报(工程科学版),2003,35(1):15-19.
[23]覃光华,李祚泳. BP网络过拟合问题研究及应用[J].武汉大学学报(工学版),2006,39(6):55-58.
[24]覃光华,丁晶,陈彬兵.预防过拟合现象的人工神经网络训练策略及其应用[J].长江科学院院报,2002,19(3):59-61.
[25] GONGA-SAHOLIARILIVA N,NEPPEL L,CHEVALLIER P,et al. Geostatistical estimation of daily monsoon precipitation at fine spatial scale:Koshi River Basin[J].Journal of Hydrologic Engineering. 2016,21(9):05016017.
[26]张继国,管耀宗,朱永忠.降雨空间分布的模糊熵聚类分析[J].河海大学学报(自然科学版). 2016,44(4):353-357.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |