Cite Article
Cite Article
MLA
Zhu, Qiliang, et al. "Deep Transfer Learning Based on LSTM Model for Reservoir Flood Forecasting." IJDWM vol.20, no.1 2024: pp.1-17. http://doi.org/10.4018/IJDWM.338912
APA
Zhu, Q., Wang, C., Jin, W., Ren, J., & Yu, X. (2024). Deep Transfer Learning Based on LSTM Model for Reservoir Flood Forecasting. International Journal of Data Warehousing and Mining (IJDWM), 20 (1), 1-17. http://doi.org/10.4018/IJDWM.338912
Chicago
Zhu, Qiliang, et al. "Deep Transfer Learning Based on LSTM Model for Reservoir Flood Forecasting," International Journal of Data Warehousing and Mining (IJDWM) 20, no.1: 1-17. http://doi.org/10.4018/IJDWM.338912
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International Journal of Data Warehousing and Mining (IJDWM) The International Journal of Data Warehousing and Mining (IJDWM) a featured IGI Global Core Journal Title, disseminates the latest international research findings in the areas of data management and analyzation. This journal is a forum for state-of-the-art developments, research, and current innovative activities focusing on the integration between the fields of data warehousing and data mining. Featured in prestigious indices including Web of Science® Citation Index Expanded®, Scopus®, Compendex®, INSPEC®, and more, this scholarly journal is led by a leading IGI Global editor and contains research from a growing list of more than 1,500+ industry-leading contributors. This journal is an ideal resource for academic researchers and practicing IT professionals looking for double-blind peer-reviewed articles that provide solutions to ongoing challenges, and new developments within this field.
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