Assessing the Applicability of Deep-Learning Method for Predicting Cyanobacteria in a Regulated River-Reference-Cited by-同舟云学术

Assessing the Applicability of Deep-Learning Method for Predicting Cyanobacteria in a Regulated River

Published:2024-05 Issue:5 Volume:150 Page:
ISSN:0733-9372
Container-title:Journal of Environmental Engineering
language:en
Short-container-title:J. Environ. Eng.

Author:

Kim Jungwook¹,Kim Hongtae²,Kim Kyunghyun²,Ahn Jung Min³

Affiliation:

1. Researcher, Water Quality Assessment Research Division, Dept. of Water Environment Research, National Institute of Environment Research, Incheon 22689, Korea.

2. Senior Researcher, Water Quality Assessment Research Division, Dept. of Water Environment Research, National Institute of Environment Research, Incheon 22689, Korea.

3. Senior Researcher, Water Quality Assessment Research Division, Dept. of Water Environment Research, National Institute of Environment Research, Incheon 22689, Korea (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JOEEDU.EEENG-7427

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4. Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

5. Akiba T. S. Sano T. Yanase T. Ohta and M. Koyama. 2019. “Optuna: A next-generation hyperparameter optimization framework.” In Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 1–10. New York: Association for Computing Machinery. https://doi.org/10.48550/arXiv.1907.10902.