Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain

被引:2
|
作者
Farfan-Duran, Juan F. [1 ]
Cea, Luis [1 ]
机构
[1] Univ A Coruna, Ctr Technol Innovat Construct & Civil Engn CITEEC, Water & Environm Engn Grp, La Coruna 15071, Spain
关键词
NEURAL-NETWORK; ANN MODELS; UNCERTAINTY; CALIBRATION;
D O I
10.1007/s12145-024-01454-9
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Accurate hourly streamflow prediction is crucial for managing water resources, particularly in smaller basins with short response times. This study evaluates six deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrids (CNN-LSTM, CNN-GRU, CNN-Recurrent Neural Network (RNN)), across two basins in Northwest Spain over a ten-year period. Findings reveal that GRU models excel, achieving Nash-Sutcliffe Efficiency (NSE) scores of approximately 0.96 and 0.98 for the Groba and Anll & oacute;ns catchments, respectively, at 1-hour lead times. Hybrid models did not enhance performance, which declines at longer lead times due to basin-specific characteristics such as area and slope, particularly in smaller basins where NSE dropped from 0.969 to 0.24. The inclusion of future rainfall data in the input sequences has improved the results, especially for longer lead times from 0.24 to 0.70 in the Groba basin and from 0.81 to 0.92 in the Anll & oacute;ns basin for a 12-hour lead time. This research provides a foundation for future exploration of DL in streamflow forecasting, in which other data sources and model structures can be utilized.
引用
收藏
页码:5289 / 5315
页数:27
相关论文
共 50 条
  • [31] Comparison Multi Transfer Learning Models for Deep Fake Image Recognizer
    Rosli, Nur Aizah
    Abdullah, Siti Norul Huda Sheikh
    Zamani, Ahmad Nazri
    Ghazvini, Anahita
    Othman, Nor Sakinah Md
    Tajuddin, Nor Alia Athirah Abdul Muariff
    2021 3RD INTERNATIONAL CYBER RESILIENCE CONFERENCE (CRC), 2021, : 38 - 43
  • [32] Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
    Serradilla, Oscar
    Zugasti, Ekhi
    Rodriguez, Jon
    Zurutuza, Urko
    APPLIED INTELLIGENCE, 2022, 52 (10) : 10934 - 10964
  • [33] Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
    Ahmed, A. A. Masrur
    Deo, Ravinesh C.
    Feng, Qi
    Ghahramani, Afshin
    Raj, Nawin
    Yin, Zhenliang
    Yang, Linshan
    JOURNAL OF HYDROLOGY, 2021, 599
  • [34] Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain
    Rehman, Aniqa
    Zhu, Jun-Jie
    Segovia, Javier
    Anderson, Paul R.
    ENERGY, 2022, 244
  • [35] Benchmarking of deep learning irradiance forecasting models from sky images - An in-depth analysis
    Paletta, Quentin
    Arbod, Guillaume
    Lasenby, Joan
    SOLAR ENERGY, 2021, 224 : 855 - 867
  • [36] Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers
    Mamede, Fabio Polola
    da Silva, Roberto Fray
    de Brito Jr, Irineu
    Yoshizaki, Hugo Tsugunobu Yoshida
    Hino, Celso Mitsuo
    Cugnasca, Carlos Eduardo
    LOGISTICS-BASEL, 2023, 7 (04):
  • [37] A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models
    Bali, Vikram
    Kumar, Ajay
    Gangwar, Satyam
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS, 2020, 11 (03) : 13 - 30
  • [38] Algal blooms forecasting with hybrid deep learning models from satellite data in the Zhoushan fishery
    Ding, Wenxiang
    Li, Changlin
    ECOLOGICAL INFORMATICS, 2024, 82
  • [39] Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
    Zhang, Yu
    Wang, Xiaodan
    Xie, Jingjing
    Bai, Yun
    TRANSPORTATION, 2024, 51 (05) : 1759 - 1784
  • [40] Rainfall variability over multiple cities of India: analysis and forecasting using deep learning models
    Panda, Jagabandhu
    Nagar, Nistha
    Mukherjee, Asmita
    Bhattacharyya, Saugat
    Singh, Sanjeev
    EARTH SCIENCE INFORMATICS, 2024, 17 (02) : 1105 - 1124