Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Reference57 articles.
1. Abba, S.I., Abdulkadir, R.A., Sammen, S.S., et al. (2021). Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration. Hydrological Sciences Journal.
2. Abba, S. I., Linh, N. T. T., Abdullahi, J., et al. (2020). Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration. IEEE Access, 8, 157218–157237.
3. Ahmed, A. A. M. (2017). Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). Journal of King Saud University-Science, 29, 151–158.
4. Anderson, C.W., & Rounds, S. (2003). Phosphorus and E. coli and their relation to selected constituents during storm runoff conditions in Fanno Creek, Oregon, 1998–99. US Department of the Interior, US Geological Survey.
5. Antanasijević, D., Pocajt, V., Povrenović, D., et al. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20, 9006–9013.
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献