Monthly discharge forecasting using wavelet neural networks with extreme learning machine  

作　　者：LI Bao Jian CHENG Chun Tian 

机构地区：[1]Institute of Hydropower System and Hydroinformatics, Dalian University of Technology

出　　处：《Science China(Technological Sciences)》2014年第12期2441-2452,共12页中国科学（技术科学英文版）

基　　金：supported by the National Science Fund for Distinguished Young Scholars,China(Grant No.51025934);the National High-Tech Research and Development Program of China(863 Program)(Grant No.2012AA050205)

摘　　要：Accurate and reliable hydrological forecasting is essential for water resource management. Feedforward neural networks can provide satisfactory forecast results in most cases, but traditional gradient-based training algorithms are usually time-consum- ing and may easily converge to local minimum. Hence, how to obtain more appropriate parameters for feedforward neural networks with more precise prediction within shorter time has been a challenging task. Extreme learning machine （ELM）, a new training algorithm for single-hidden layer feedforward neural networks （SLFNs）, has been proposed to avoid these disad- vantages. In this study, a conjunction model of wavelet neural networks with ELM （WNN-ELM） is proposed for 1-month ahead discharge forecasting, The ~ trous wavelet transform is used to decompose the original discharge time series into several sub-series. The sub-series are then used as inputs for SLFNs coupled with ELM algorithm （SLFNs^ELM）; the output is the next step observed discharge. For comparison, the SLFNs-ELM and support vector machine （SVM） are also employed. Monthly discharge time series data from two reservoirs in southwestern China are derive] for validating the models. In addi- tion, four quantitative standard statistical performance evaluation measures are utilized to evaluate the model performance. The results indicate that the SLFNs-ELM performs slightly better than the SVM for peak discharge estimation, and the proposed model WNN-ELM provides more accurate forecast precision than SLFNs-ELM and SVM

关 键 词：monthly discharges discrete wavelet transform extreme learning machine forecasting 

分 类 号：P338[天文地球—水文科学] TP18[水利工程—水文学及水资源]