Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026262 |
Ensemble‐based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection | |
Taereem Kim; Ju‐; Young Shin; Hanbeen Kim; Jun‐; Haeng Heo | |
2020-04-25 | |
发表期刊 | Water Resources Research
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出版年 | 2020 |
英文摘要 | Artificial neural networks (ANNs) have been extensively used to forecast monthly precipitation for water resources management over the past few decades. Efforts to produce more accurate and stable forecasts face ongoing challenges as the so‐called single ANN (S‐ANN) approach has several limitations, particularly regarding uncertainty. Many attempts have been made to deal with different types of uncertainties by applying ensemble approaches. Here, we propose a new ANN ensemble model (ANN‐ENS) dealing with uncertainty in model structure and input variable selection to provide a more accurate and stable forecasting performance. This model is structured by generating various input layers, considering all the candidate input variables (i.e., large‐scale climate indices and lagged precipitation). We developed a modified backward elimination method to select the preliminary input variables from all the candidate input variables. Then, we tested and validated the proposed ANN‐ENS using observed monthly precipitation from 10 meteorological stations in the Han River basin, South Korea. Our results demonstrated that the ANN‐ENS enhanced the forecasting performance in terms of both accuracy and stability. Although a significant uncertainty was introduced by using all the candidate input variables, the forecasting result outperformed S‐ANNs for all employed stations. Additionally, the ANN‐ENS provided a more stable forecasting performance in comparison with S‐ANNs, which are highly sensitive. Moreover, the generated ensemble members were slightly biased at some stations, but were generally reliable. |
领域 | 资源环境 |
URL | 查看原文 |
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/249180 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Taereem Kim,Ju‐,Young Shin,等. Ensemble‐based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection[J]. Water Resources Research,2020. |
APA | Taereem Kim,Ju‐,Young Shin,Hanbeen Kim,Jun‐,&Haeng Heo.(2020).Ensemble‐based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection.Water Resources Research. |
MLA | Taereem Kim,et al."Ensemble‐based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection".Water Resources Research (2020). |
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