The exploration of a Temporal Convolutional Network combined with Encoder-Decoder framework for runoff forecasting

Lin, Kangling; Sheng, Sheng; Zhou, Yanlai; Liu, Feng; Li, Zhiyu; Chen, Hua; Xu, Chong‐Yu; Chen, Jie

doi:10.2166/nh.2020.100

Cited by 30 publications

(14 citation statements)

References 36 publications

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“…Similarly, LightGBM has performed well in financial forecasting and cancer patient classification [36] [37]. Although TCN is a recently proposed model, it is also widely used in the fields of the weather forecasting [38], runoff forecasting [39], etc.…”

Section: ) Methods For Comparisonmentioning

confidence: 99%

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

et al. 2022

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Fishery catch forecasting is a crucial aspect of aquatic research because of its relevance to establishing effective fishery management and resource allocation systems. In this study, we aim to forecast and analyze fish catch by collaboratively processing data using methods at multiple scales. To this end, we propose two computational fishery catch forecasting models. A neural network model based on the multi-timescale features of a convolutional neural network and a long short-term memory neural network is proposed and implemented to forecast short-term measures for the daily catch in the eastern ports of Hokkaido, Japan. Similarly, we propose a long-term catch forecasting and analysis model combining the autoregressive integrated moving average (ARIMA) method and a neural network to explore short-term water temperature and long-term catch dependence in the case of sparse data; we implement this method to investigate the total monthly catch in Hokkaido. The experimental results demonstrate that the proposed methods were able to effectively forecast and analyze fishery catch based on different data scales, volumes, and other complex situations. This is also the first work in the field that considers multiple perspectives.INDEX TERMS ARIMA model, fishery catch forecasting, neural network.

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Section: ) Methods For Comparisonmentioning

confidence: 99%

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

et al. 2022

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“…In this study, the VIC model was verified through a comparison between the simulated stream flows and the observations. The Nash-Sutcliffe efficiency (NSE), relative error (Bias), and determination coefficient (R 2 ) were used for the evaluation of model accuracy (Zhou et al 2019(Zhou et al , 2020Kao et al 2020;Lin et al 2020). A significant coincidence rate with P , 0.05 was detected in both the calibration and validation (Figure 2), demonstrating that the VIC-based ETa is adaptable and rational for the subsequent drought analysis.…”

Section: Vic Modelmentioning

confidence: 98%

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

Zhou

2021

Hydrology Research

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Understanding the spatiotemporal characteristics of drought events and their impacts on terrestrial net primary productivity (NPP) is crucial for drought mitigation and environmental protection. This study, by taking the Pearl River basin as the case region, investigated drought duration, severity, intensity, affected area, and centroids during 1960–2015 based on the Standardized Evapotranspiration Deficit Index and three-dimensional clustering algorithm and then revealed how these drought characteristics have affected NPP. Results showed that there were altogether 32 severe drought events lasting at least 3 months in the basin, with half lasting longer than 6 months. The total NPP loss significantly correlated with drought severity and intensity. Most drought events caused reduce in NPP across more than half of the drought-affected area; specifically, the February–December drought in 2011 has cut NPP by 31.85 Tg C, accounting for 11.7% of the regional annual mean NPP, while the September 2009–September 2010 drought caused a decrease of 20.26 Tg C in NPP. Our research improves the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions.

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“…With gated TCN layers, Deep Mind researchers developed a model named WaveNet for the speech recognition of the voice wave. And recent studies have shown that TCN models work well on rainfall-runoff modeling as well (Lin et al, 2020; Van et al, 2020;Xu et al, 2021). However, these studies developed lumped models by considering each watershed as a unit, and all available geospatial information is not used.…”

Section: Introductionmentioning

confidence: 96%

Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

Xiang¹,

Demir²

2022

Preprint

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Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

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The exploration of a Temporal Convolutional Network combined with Encoder-Decoder framework for runoff forecasting

Cited by 30 publications

References 36 publications

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

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