基于正则化GRU模型的洪水预测
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摘要
针对传统神经网络模型在洪水预测过程中存在准确性低、过拟合等问题,本文以赣江流域外洲水文站每月平均水位为研究对象,提出基于正则化GRU神经网络的洪水预测模型来提高洪水预报精度.选用relu函数作为整个神经网络的输出层激活函数,将弹性网正则化引入到GRU模型中,对网络中输入权重w实施正则化处理,以提升GRU模型的泛化性能,并将该模型应用于外洲水文站每月平均水位的拟合及预测.实验对比表明,弹性网正则化优化后的模型预测拟合程度较高,合格率提高了9.3%,计算出的均方根误差较小.
Aiming at the problems of low accuracy and over-fitting of traditional neural network model in flood forecasting process, this study takes the monthly average water level of Waizhou Hydrological Station in Ganjiang River Basin as the research object, and proposes a flood forecasting model based on regularized GRU neural network to improve the accuracy of flood forecasting. Relu function is selected as the output layer activation function of the whole neural network. To improve the generalization performance of GRU model, regularization of elastic network is introduced into GRU model, and regularizes the input weights in the network. The model is applied to the fitting and prediction of the monthly average water level at Waizhou Hydrological Station, and the experimental comparison shows that the model optimized by regularization of elastic network has a higher fitting degree, the qualified rate is increased by 9.3%, and the calculated root mean square error is small.
Aiming at the problems of low accuracy and over-fitting of traditional neural network model in flood forecasting process, this study takes the monthly average water level of Waizhou Hydrological Station in Ganjiang River Basin as the research object, and proposes a flood forecasting model based on regularized GRU neural network to improve the accuracy of flood forecasting. Relu function is selected as the output layer activation function of the whole neural network. To improve the generalization performance of GRU model, regularization of elastic network is introduced into GRU model, and regularizes the input weights in the network. The model is applied to the fitting and prediction of the monthly average water level at Waizhou Hydrological Station, and the experimental comparison shows that the model optimized by regularization of elastic network has a higher fitting degree, the qualified rate is increased by 9.3%, and the calculated root mean square error is small.
引文
1范睿.基于遗传算法的神经网络洪水预报研究与应用[硕士学位论文].哈尔滨:哈尔滨工程大学,2005.
2Lippmann RP.An introduction to computing with neural nets.ACM SIGARCH Computer Architecture News,1988,16(1):7-25.[doi:10.1145/44571]
3李晓丽,周小健,沈钢纲,等.不确定支持向量机在洪水预测模型中的应用.兰州理工大学学报,2012,38(3):107-110.[doi:10.3969/j.issn.1673-5196.2012.03.025]
4梁存峰.基于混沌Volterra自适应模型的洪水预测研究.水资源与水工程学报,2011,22(1):146-150.
5金保明.BP神经网络在闽江十里庵流量预测中的应用.水电能源科学,2010,28(9):12-14.[doi:10.3969/j.issn.1000-7709.2010.09.004]
6何勇,李妍琰.改进粒子群优化BP神经网络的洪水智能预测模型研究.西南师范大学学报(自然科学版),2014,39(5):75-80.
7叶小舟,陶飞飞,戚荣志,等.循环神经网络结构中激活函数的改进.计算机与现代化,2016,(12):29-33.[doi:10.3969/j.issn.1006-2475.2016.12.006]
8Semeniuta S,Severyn A,Barth E.Recurrent dropout without memory loss.arXiv preprint arXiv:1603.05118,2016.
9刘洋.基于GRU神经网络的时间序列预测研究[硕士学位论文].成都:成都理工大学,2017.
10张玉环,钱江.基于两种LSTM结构的文本情感分析.软件,2018,39(1):116-120.[doi:10.3969/j.issn.1003-6970.2018.01.023]
11王鑫,吴际,刘超,等.基于LSTM循环神经网络的故障时间序列预测.北京航空航天大学学报,2018,44(4):772-784.
12刘建伟,崔立鹏,刘泽宇,等.正则化稀疏模型.计算机学报,2015,38(7):1307-1325.
13崔东文.多隐层BP神经网络模型在径流预测中的应用.水文,2013,33(1):68-73.[doi:10.3969/j.issn.1000-0852.2013.01.013]
14丁海蛟.基于LS-SVM的河道洪水预报研究[硕士学位论文].昆明:昆明理工大学,2016.
15孙卫刚,王正勇.克尔古提水文站站月水量预报方案的编制.大科技·科技天地,2010.
16朱星明,卢长娜,王如云,等.基于人工神经网络的洪水水位预报模型.水利学报,2005,36(7):806-811.[doi:10.3321/j.issn:0559-9350.2005.07.007]
17中华人民共和国水利部.SL 250-2000水文情报预报规范.北京:中国水利水电出版社,2001.
2Lippmann RP.An introduction to computing with neural nets.ACM SIGARCH Computer Architecture News,1988,16(1):7-25.[doi:10.1145/44571]
3李晓丽,周小健,沈钢纲,等.不确定支持向量机在洪水预测模型中的应用.兰州理工大学学报,2012,38(3):107-110.[doi:10.3969/j.issn.1673-5196.2012.03.025]
4梁存峰.基于混沌Volterra自适应模型的洪水预测研究.水资源与水工程学报,2011,22(1):146-150.
5金保明.BP神经网络在闽江十里庵流量预测中的应用.水电能源科学,2010,28(9):12-14.[doi:10.3969/j.issn.1000-7709.2010.09.004]
6何勇,李妍琰.改进粒子群优化BP神经网络的洪水智能预测模型研究.西南师范大学学报(自然科学版),2014,39(5):75-80.
7叶小舟,陶飞飞,戚荣志,等.循环神经网络结构中激活函数的改进.计算机与现代化,2016,(12):29-33.[doi:10.3969/j.issn.1006-2475.2016.12.006]
8Semeniuta S,Severyn A,Barth E.Recurrent dropout without memory loss.arXiv preprint arXiv:1603.05118,2016.
9刘洋.基于GRU神经网络的时间序列预测研究[硕士学位论文].成都:成都理工大学,2017.
10张玉环,钱江.基于两种LSTM结构的文本情感分析.软件,2018,39(1):116-120.[doi:10.3969/j.issn.1003-6970.2018.01.023]
11王鑫,吴际,刘超,等.基于LSTM循环神经网络的故障时间序列预测.北京航空航天大学学报,2018,44(4):772-784.
12刘建伟,崔立鹏,刘泽宇,等.正则化稀疏模型.计算机学报,2015,38(7):1307-1325.
13崔东文.多隐层BP神经网络模型在径流预测中的应用.水文,2013,33(1):68-73.[doi:10.3969/j.issn.1000-0852.2013.01.013]
14丁海蛟.基于LS-SVM的河道洪水预报研究[硕士学位论文].昆明:昆明理工大学,2016.
15孙卫刚,王正勇.克尔古提水文站站月水量预报方案的编制.大科技·科技天地,2010.
16朱星明,卢长娜,王如云,等.基于人工神经网络的洪水水位预报模型.水利学报,2005,36(7):806-811.[doi:10.3321/j.issn:0559-9350.2005.07.007]
17中华人民共和国水利部.SL 250-2000水文情报预报规范.北京:中国水利水电出版社,2001.