Abstract
As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL.
Highlights
Machine Learning (ML) techniques have obtained growing attention and Deep Learning (DL) techniques have led to substantial changes across many areas of study [1]
To evaluate the interactions between Long Short-Term Memory (LSTM) networks and ML architecture, a novel ML architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with another conventional architecture
The Nash–Sutcliffe coefficient of efficiency (NSE) and Relative Peak‐Discharge Error (RPE) for different sample sets at 1–10-h lead-time are summarized in box-and-whisker plots in Figures 10 and 11, and each boxplot includes 300 simulated results from 75 flood events by four combination schemes, which consist of two Recurrent Neural Networks (RNNs) approaches and two model architectures
Summary
Machine Learning (ML) techniques have obtained growing attention and Deep Learning (DL) techniques have led to substantial changes across many areas of study [1]. As a subfield of ML, DL is more concerned with datasets, algorithms and layered structures. Owing to the enhanced capability to characterize the system complexity and flexible structure, research and applications of ML/DL for time-series prediction are proliferating and promising. The advent of ML/DL techniques has encouraged novel applications or substantially improved old ones [2,3]. Water 2020, 12, 912 approaches are fundamentally black-box methods, they can be developed with minimal inputs and applied without considering the redundant physical mechanism of the watershed system. ML approaches and their hybrid application have performed well or comparable when applied to predict hydrological time series, compared to physically based models [4]. The application of ML/DL techniques depends heavily on datasets, algorithms and layered structures, so it is necessary to quantify uncertainty contributions of each component in ML/DL modeling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
