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Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation

Author

Listed:
  • Anbang Peng

    (Nanjing Hydraulic Research Institute)

  • Xiaoli Zhang

    (North China University of Water Resources and Electric Power)

  • Wei Xu

    (Chongqing Jiaotong University)

  • Yuanyang Tian

    (Nanjing Hydraulic Research Institute
    Chongqing Jiaotong University)

Abstract

With the rapid development of Artificial Intelligence (AI) technology, the Long Short-Term Memory (LSTM) network has been widely used for forecasting hydrological process. To evaluate the effect of training data amount on the performance of LSTM, the study proposed an experiment scheme. First, K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series of 130 years based on the observed data, and the Soil and Water Assessment Tool (SWAT) model is used to obtain the corresponding runoff series with the generated meteorological data series. Then, the 130 years of rainfall and runoff data is divided into two parts: the first 80 years of data for model training and the remaining 50 years of data for model verification. Finally, the LSTM models are developed and evaluated, with the first 5-year, 10-year, 20-year, 40-year and 80-year data series as training data respectively. The results obtained in Yalong River, Minjiang River and Jialing River show that increasing the training data amount can effectively reduce the over-fittings of LSTM network and improve the prediction accuracy and stability of LSTM network.

Suggested Citation

  • Anbang Peng & Xiaoli Zhang & Wei Xu & Yuanyang Tian, 2022. "Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2381-2394, May.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:7:d:10.1007_s11269-022-03148-7
    DOI: 10.1007/s11269-022-03148-7
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    References listed on IDEAS

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    1. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
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    Cited by:

    1. Yiming Wei & Renchao Wang & Ping Feng, 2024. "Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2471-2488, May.

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