Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2017.06.014 |
Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm | |
Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek | |
2017-11-15 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2017 |
卷号 | 197 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | Forecasting streamflow is vital for strategically planning, utilizing and redistributing water resources. In this paper, a wavelet-hybrid artificial neural network (ANN) model integrated with iterative input selection (IIS) algorithm (IIS-W-ANN) is evaluated for its statistical preciseness in forecasting monthly streamflow, and it is then benchmarked against M5 Tree model. To develop hybrid IIS-W-ANN model, a global predictor matrix is constructed for three local hydrological sites (Richmond, Gwydir, and Darling River) in Australia's agricultural (Murray-Darling) Basin. Model inputs comprised of statistically significant lagged combination of streamflow water level, are supplemented by meteorological data (i.e., precipitation, maximum and minimum temperature, mean solar radiation, vapor pressure and evaporation) as the potential model inputs. To establish robust forecasting models, iterative input selection (US) algorithm is applied to screen the best data from the predictor matrix and is integrated with the non-decimated maximum overlap discrete wavelet transform (MODWT) applied on the IIS-selected variables. This resolved the frequencies contained in predictor data while constructing a wavelet-hybrid (i.e., IIS-W-ANN and IIS-W-M5 Tree) model. Forecasting ability of US-W-ANN is evaluated via correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe Efficiency (E-NS), root-mean-square-error (RMSE), and mean absolute error (MAE), including the percentage RMSE and MAE. While ANN models are seen to outperform M5 Tree executed for all hydrological sites, the IIS variable selector was efficient in determining the appropriate predictors, as stipulated by the better performance of the IIS coupled (ANN and M5 Tree) models relative to the models without IIS. When IIS-coupled models are integrated with MODWT, the wavelet-hybrid IIS-W-ANN and IIS-W-M5 Tree are seen to attain significantly accurate performance relative to their standalone counterparts. Importantly, IIS-W-ANN model accuracy outweighs IIS-ANN, as evidenced by a larger r and WI (by 7.5% and 3.8%, respectively) and a lower RMSE (by 21.3%). In comparison to the IIS-W-M5 Tree model, IIS-W-ANN model yielded larger values of WI = 0.936-0.979 and E-NS = 0.770-0.920. Correspondingly, the errors (RMSE and MAE) ranged from 0.162-0.487 m and 0.139-0.390 m, respectively, with relative errors, RRMSE = (15.65-21.00) % and MAPE = (14.79-20.78) %. Distinct geographic signature is evident where the most and least accurately forecasted streamflow data is attained for the Gwydir and Darling River, respectively. Conclusively, this study advocates the efficacy of iterative input selection, allowing the proper screening of model predictors, and subsequently, its integration with MODWT resulting in enhanced performance of the models applied in streamflow forecasting. |
英文关键词 | Wavelet-hybrid model Streamflow forecasting Drought in Murray-Darling Basin Iterative input selection algorithm, MODWT |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000412250700003 |
WOS关键词 | SUPPORT VECTOR MACHINE ; ARTIFICIAL NEURAL-NETWORK ; DISCRETE WAVELET TRANSFORM ; GLOBAL SOLAR-RADIATION ; M5 MODEL TREES ; VARIABLE SELECTION ; PARTICLE SWARM ; AUSTRALIAN RAINFALL ; UPPER REACH ; SHORT-TERM |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/38444 |
专题 | 地球科学 |
作者单位 | Univ Southern Queensland, Inst Agr & Environm IAg&E, Sch Agr Computat & Environm Sci, Springfield, Qld 4300, Australia |
推荐引用方式 GB/T 7714 | Prasad, Ramendra,Deo, Ravinesh C.,Li, Yan,et al. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm[J]. ATMOSPHERIC RESEARCH,2017,197. |
APA | Prasad, Ramendra,Deo, Ravinesh C.,Li, Yan,&Maraseni, Tek.(2017).Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm.ATMOSPHERIC RESEARCH,197. |
MLA | Prasad, Ramendra,et al."Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm".ATMOSPHERIC RESEARCH 197(2017). |
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