Parallel Chaos Search Based Incremental Extreme Learning Machine Based Empirical Wavelet Transform: A New Hybrid Machine Learning Model for River Dissolved Oxygen Forecasting-Reference-Cited by-同舟云学术

Parallel Chaos Search Based Incremental Extreme Learning Machine Based Empirical Wavelet Transform: A New Hybrid Machine Learning Model for River Dissolved Oxygen Forecasting

Published:2022 Issue: Volume: Page:355-376
ISSN:1860-949X
Container-title:Computational Intelligence for Water and Environmental Sciences
language:
Short-container-title:

Author:

Heddam Salim^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2519-1_17

Reference30 articles.

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2. Asadollah, S. B. H. S., Sharafati, A., Motta, D., & Yaseen, Z. M. (2021). River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering, 9(1), 104599. https://doi.org/10.1016/j.jece.2020.104599

3. Chen, Z., Xu, H., Jiang, P., Yu, S., Lin, G., Bychkov, I., Hmelnov, A., Ruzhnikov, G., Zhu, N., & Liu, Z., (2021). A transfer learning-based LSTM strategy for imputing large-scale consecutive missing data and its application in a water quality prediction system. Journal of Hydrology, 126573. https://doi.org/10.1016/j.jhydrol.2021.126573

4. Dash, D. R., Dash, P. K., & Bisoi, R. (2021). Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm. Renewable Energy, 174, 513–537. https://doi.org/10.1016/j.renene.2021.04.088

5. Dehghani, R., Torabi Poudeh, H., & Izadi, Z. (2021). Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques. Modeling Earth Systems and Environment, 1–15. https://doi.org/10.1007/s40808-021-01253-x

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