https://scholars.lib.ntu.edu.tw/handle/123456789/641705
標題: | Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting | 作者: | Luo, Yuxuan Zhou, Yanlai Chen, Hua Xiong, Lihua Guo, Shenglian FI-JOHN CHANG |
關鍵字: | Deep Learning | Hydro-meteorological heterogeneity | Long Short-Term Memory (LSTM) | Multi-step-ahead flood forecasts | Spatiotemporal Hetero Graph | 公開日期: | 1-四月-2024 | 卷: | 633 | 來源出版物: | Journal of Hydrology | 摘要: | Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation of hydro-meteorological variables is fundamentally challenging for artificial neural networks. This challenge becomes even more significant when the complexity involved introduces systematic biases and time-lag phenomena in flood forecasts. For the first time, this study proposed a Spatiotemporal Hetero Graph-based Long Short-term Memory (SHG-LSTM) model for multi-step-ahead flood forecasting. The case study focused on the Jianxi basin in China. 25,341 hydro-meteorological data, with a temporal resolution of three hours, collected during flood events were divided into training and test datasets for model construction purpose. The model was fed with 3-h streamflow and precipitation data from 23 gauge stations, covering a time span of the preceding 21 h, for generating flood forecasts at 1 up to 7 horizons. To make a comparative analysis, both LSTM and the Spatiotemporal Graph Convolutional Network (S-GCN) were constructed. This study conducted multiple rounds of model training with varying initial parameters to assess the accuracy, stability, and reliability of the LSTM, S-GCN, and SHG-LSTM models. The results demonstrated that the SHG-LSTM model outperformed LSTM and S-GCN models, with an average reduction in the volume error (VE) of 6.5% and 11.1%, respectively, a decrease in the Mean Absolute Error (MAE) of 6.7% and 8.1%, respectively, and a reduction in the Root Mean Square Error (RMSE) of 5.0% and 12.9%, respectively. Furthermore, the SHG-LSTM model not only could efficiently overcome the under-prediction bottleneck, but also could largely mitigate the time-lag phenomenon in flood forecasts, even during the testing stages. These findings indicate that the proposed SHG-LSTM model can provide a general framework for modelling the spatial heterogeneity and correlation of hydro-meteorological variables and achieve accurate and reliable flood forecasts, thereby enhancing the model's applicability in flood prevention platforms and systems. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186633198&doi=10.1016%2fj.jhydrol.2024.130937&partnerID=40&md5=64649e25fac4974831e91cc7e26b2fe9 https://scholars.lib.ntu.edu.tw/handle/123456789/641705 |
ISSN: | 00221694 | DOI: | 10.1016/j.jhydrol.2024.130937 |
顯示於: | 生物環境系統工程學系 |
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