دورية أكاديمية
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
العنوان: | Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models |
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المؤلفون: | T. Lees, M. Buechel, B. Anderson, L. Slater, S. Reece, G. Coxon, S. J. Dadson |
المصدر: | Hydrology and Earth System Sciences, Vol 25, Pp 5517-5534 (2021) |
بيانات النشر: | Copernicus Publications, 2021. |
سنة النشر: | 2021 |
المجموعة: | LCC:Technology LCC:Environmental technology. Sanitary engineering LCC:Geography. Anthropology. Recreation LCC:Environmental sciences |
مصطلحات موضوعية: | Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350 |
الوصف: | Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on catchments in Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns in model performances to improve our understanding of hydrological processes and to examine the advantages and disadvantages of LSTM-based models for hydrological simulation. By training two LSTM architectures across a large sample of 669 catchments in GB, we demonstrate that the LSTM and the Entity Aware LSTM (EA LSTM) models simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores of 0.88 and 0.86 respectively. We find that the LSTM-based models outperform a suite of benchmark conceptual models, suggesting an opportunity to use additional data to refine conceptual models. In summary, the LSTM-based models show the largest performance improvements in the north-east of Scotland and in south-east of England. The south-east of England remained difficult to model, however, in part due to the inability of the LSTMs configured in this study to learn groundwater processes, human abstractions and complex percolation properties from the hydro-meteorological variables typically employed for hydrological modelling. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1027-5606 1607-7938 |
Relation: | https://hess.copernicus.org/articles/25/5517/2021/hess-25-5517-2021.pdf; https://doaj.org/toc/1027-5606; https://doaj.org/toc/1607-7938 |
DOI: | 10.5194/hess-25-5517-2021 |
URL الوصول: | https://doaj.org/article/52439268c3c94462808e3826ed5337bd |
رقم الأكسشن: | edsdoj.52439268c3c94462808e3826ed5337bd |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 10275606 16077938 |
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DOI: | 10.5194/hess-25-5517-2021 |