期刊文献+

基于小波神经网络的东江流域径流模拟多模型比较研究 被引量:5

Multiple Models Comparative Study of the East River Basin Runoff Simulation Based on Wavelet Neural Network
原文传递
导出
摘要 东江流域为香港及广州等特大城市的重要水源地,水文过程模拟及预测对流域水资源开发与管理具有重要理论与现实意义.本研究针对小波神经网络中最佳母小波和最佳等级选择问题,在与传统模型比较基础上,研究得出东江流域径流模拟的最佳小波神经网络模型,并以此进行东江流域的径流预测分析.结果表明:1)选择恰当的母小波可以有效捕捉信号统计特征,该流域蒸发量、降水量、温度和湿度的最佳分解母小波分别为Db4,Sym2,Db9及Db4小波,其小波分解最佳等级为5;2)小波神经网络作为新型混合优化模型,在母小波选择和分解等级确定后,经东江博罗站径流模拟分析,确定为东江流域最佳小波神经网络模拟模型.该研究用于东江径流的预测,效果在满意范围内. As the critical water source of large cities such as Hong Kong and Guangzhou,hydrological process simulation of East River Basin has important theoretical and practical significance on water resources development and management.Therefore,this research focuses on the optimum mother wavelet and level of a new hybrid model wavelet artificial neutral network(WANN).The relative performance of wavelet artificial neutral network is compared with the regular traditional models for runoff simulation and prediction in East River Basin.The results indicate that:1)the mother wavelet is useful for capturing statistical characteristics of time series,and the optimal mother wavelet for evaporation,precipitation,temperature and humidity is Db4,Sym2,Db9,Db4,the best decomposed level is five;2)through selecting the best mother wavelet and level,the prediction accuracy of WANN is better than other traditional models.Using this result to predict runoff in Dongjiang Basin is within the expected range.
出处 《武汉大学学报(理学版)》 CAS CSCD 北大核心 2015年第3期255-261,共7页 Journal of Wuhan University:Natural Science Edition
基金 国家杰出青年科学基金项目(51425903) 香港特别行政区研究资助局项目(CUHK441313)
关键词 小波神经网络 径流模拟及预测 母小波 分解等级 模型比较 wavelet artificial neutral network runoff simulation and prediction mother wavelet decomposed level model comparation
  • 引文网络
  • 相关文献

参考文献17

  • 1Makkeasorn A, Chang N B, Zhou X. Short-term stre- amflow forecasting with global climate change implica- tions--A comparative study between genetic program- ming and neural network models[J]. Journal of Hy- drology ,2008, 352(3-4), 336-354.
  • 2Nourani V, Kisi O, Komasi M. Two hybrid artificial intelligence approaches for modeling rainfall-runoff process[J]. Journal of Hydrology, 2011, 402 (1) : 41-59.
  • 3Wu M C, Lin G F, Lin H Y. Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map [J]. Hydrological Processes, 2014, 28(2) : 386-397.
  • 4Adamowski J, Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid water- sheds[J]. Journal of Hydrology, 2010, 390(1): 85- 91.
  • 5Nourani V, Komasi M, Mano A. A multivariate ANN-wavelet approach for rainfall-runoff modeling[J]. Water Resources Management, 2009, 23 (14) .. 2877- 2894.
  • 6Nourani V, Alami M T, Aminfar M H. A combined neural-wavelet model for prediction of Ligvanchai wa- tershed precipitation[J]. Engineering Applications of Artificial Intelligence, 2009, 22(3): 466-472.
  • 7Zhang Q, Singh V P, Li K, et al. Trend, periodicity and abrupt change in streamflow of the East River, the Pearl River basin[J]. Hydrological Processes, 2014, 28(2) : 305-314.
  • 8林凯荣,何艳虎,陈晓宏.气候变化及人类活动对东江流域径流影响的贡献分解研究[J].水利学报,2012,43(11):1312-1321. 被引量:56
  • 9石教智,陈晓宏,吴甜.东江流域降雨径流变化趋势及其原因分析[J].水电能源科学,2005,23(5):8-10. 被引量:38
  • 10Lin J Y, Cheng C T, Chau K W. Using support vector machines for long-term discharge prediction [J]. Hydrological Sciences Journal, 2006, 51 (4) : 599- 612.

二级参考文献36

共引文献125

同被引文献85

引证文献5

二级引证文献51

;
使用帮助 返回顶部