Mathematical simulation for algal growth in the water reservoirs of Moncton city (New Brunswick, Canada) by the supervised learning classifier
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Mathematical simulation for algal growth in the water reservoirs of Moncton city (New Brunswick, Canada) by the supervised learning classifier
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| Creator |
Sabir, Qurat-Ul An
Nadeem, Muhammad Nguyen-Quang, Tri |
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| Contributor |
Dalhousie University
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| Subject |
Artificial Neural Network (ANN)
Cyanobacteria Harmful Algal Blooms (HAB) Modified Redfield Ratio (MRR) Supervised learning classifier |
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| Description |
Mathematical model is a good approach to deal with the coupling effects of governing parameters in algal bloom growth. Among manymodels to deal with combining factors and data-based supervised learning classifiers, the Artificial Neural Network (ANN) has the most significant impact on the development of bloom pattern. The objective of this paper is to use the Artificial Neural Network (ANN) model to simulate the growth of harmful algae under environmental factors that can lead to bloom pattern in two reservoirs of Moncton city (Canada) with the collected data fromtwo years of observation 2016–2017. |
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| Date |
2019-03-25T11:16:03Z
2019-03-25T11:16:03Z 2018-02-01 2018-02-01 |
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| Type |
Article
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| Identifier |
Sabir Q. A. Mathematical simulation for algal growth in the water reservoirs of Moncton city (New Brunswick, Canada) by the supervised learning classifier / Qurat-Ul An Sabir, Muhammad Nadeem, Tri Nguyen-Quang // Environmental Problems. — Lviv : Lviv Politechnic Publishing House, 2018. — Vol 3. — No 2. — P. 103–114.
http://ena.lp.edu.ua:8080/handle/ntb/44781 Sabir Q. A. Mathematical simulation for algal growth in the water reservoirs of Moncton city (New Brunswick, Canada) by the supervised learning classifier / Qurat-Ul An Sabir, Muhammad Nadeem, Tri Nguyen-Quang // Environmental Problems. — Lviv : Lviv Politechnic Publishing House, 2018. — Vol 3. — No 2. — P. 103–114. |
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| Language |
en
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| Relation |
Environmental Problems, 2 (3), 2018
[1] Huang, W., & Foo, S. (2002). Neural network modeling of salinity variation in Apalachicola River. Water Research, 36(1), 356–362. [2] Maier, H. R., and Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101–124. [3] McCulloch, W. S. and Pitts, W. (1943), “A logical calculus of the ideas immanent in nervous activity”, The bulletin of mathematical biophysics, Vol. 5, No. 4,pp. 115–133. [4] Torrecilla, J. S., Otero, L., & Sanz, P. D. (2004). A neural network approach for thermal/pressure food processing. Journal of Food Engineering, 62(1), 89–95. [5] Madic, M. J., & Radovanović, M. R. (2011). Optimal selection of ANN training and architectural parameters using Taguchi method: A case study. FME Transactions,39(2), 79–86. [6] Elangasinghe, M. A., Singhal, N., Dirks, K. N., & Salmond, J. A. (2014). Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmospheric pollution research, 5(4), 696–708. [7] Pandey, D. S., Das, S., Pan, I., Leahy, J. J., & Kwapinski, W. (2016). Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor. Waste management, 58, 202–213. [8] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137. [9] Khademi, F., Akbari, M., Jamal, S. M., & Nikoo, M. (2017). Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 11(1), 90–99. [1] Huang, W., & Foo, S. (2002). Neural network modeling of salinity variation in Apalachicola River. Water Research, 36(1), 356–362. [2] Maier, H. R., and Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101–124. [3] McCulloch, W. S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", The bulletin of mathematical biophysics, Vol. 5, No. 4,pp. 115–133. [4] Torrecilla, J. S., Otero, L., & Sanz, P. D. (2004). A neural network approach for thermal/pressure food processing. Journal of Food Engineering, 62(1), 89–95. [5] Madic, M. J., & Radovanović, M. R. (2011). Optimal selection of ANN training and architectural parameters using Taguchi method: A case study. FME Transactions,39(2), 79–86. [6] Elangasinghe, M. A., Singhal, N., Dirks, K. N., & Salmond, J. A. (2014). Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmospheric pollution research, 5(4), 696–708. [7] Pandey, D. S., Das, S., Pan, I., Leahy, J. J., & Kwapinski, W. (2016). Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor. Waste management, 58, 202–213. [8] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137. [9] Khademi, F., Akbari, M., Jamal, S. M., & Nikoo, M. (2017). Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 11(1), 90–99. |
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| Rights |
© Національний університет „Львівська політехніка“, 2018
© Qurat-Ul An Sabir, Tri Nguyen-Quang, 2018 |
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| Format |
103-114
12 application/pdf image/png |
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| Coverage |
Lviv
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| Publisher |
Lviv Politechnic Publishing House
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