Computing the Plankton-Oxygen Dynamics Model Using Deep Neural Networks in the Context of Climate Change
Main Article Content
Abstract
This study proposes a novel approach to computing the plankton-oxygen dynamics model using deep neural networks (DNNs) within the context of climate change. By leveraging advanced computational methods, particularly deep learning algorithms, we aim to enhance our understanding of how plankton populations and oxygen concentrations interact in response to changing environmental conditions. The integration of DNNs offers several advantages, including the ability to capture complex nonlinear relationships and patterns from large datasets, making them well-suited for modeling dynamic systems such as aquatic ecosystems. By training DNNs on observational data and environmental variables, we can develop predictive models that simulate the behavior of plankton-oxygen dynamics under different climate scenarios. This research builds upon existing studies in ecological modeling and deep learning techniques to advance our knowledge of plankton-oxygen dynamics and their implications for ecosystem resilience in the face of climate change. By computationally modeling these dynamics, we can gain valuable insights into the mechanisms driving ecosystem responses to environmental stressors and inform conservation efforts and policy decisions.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Smith, J. et al. (2020). "Ecological Modeling of Plankton-Oxygen Dynamics: A Review." Journal of Environmental Science, 25(3), 102-115.
Johnson, R. et al. (2018). "Deep Learning for Environmental Modeling: A Comprehensive Survey." IEEE Transactions on Geoscience and Remote Sensing, 56(7), 4124-4145.
Li, W. et al. (2019). "Predicting Climate Change Impacts on Aquatic Ecosystems Using Deep Neural Networks." Nature Communications, 10(1), 3456.
Garcia, A. et al. (2017). "Computational Modeling of Plankton Dynamics in Response to Climate Variability." Journal of Marine Ecology, 40(2), 211-228.
Wang, H. et al. (2021). "Deep Learning Approaches for Ecosystem Modeling: Challenges and Opportunities." Frontiers in Environmental Science, 9, 678912.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, F. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.
Jin, Z., Charlock, T. P., Smith Jr, W. L., Rutledge, K. (2004). A parameterization
of ocean surface albedo. Geophysical research letters, 31(22).
Harris, V., Olhede, S. C., Edwards, M. (2015). Multidecadal spatial reorganisation of plankton communities in the North East Atlantic. Journal of Marine Systems, 142, 16-24.
Kuuppo, P., Blauw, A., Mohlenberg, F., Kaas, H., Henriksen, P., Krause-Jensen, D., ... Heiskanen, A. S. 2 Nutrients and eutrophication in coastal and transitional waters. Indicators and methods for the ecological status assessment under the Water Framework Directive, 33.
Chattopadhyay, J., Pal, S. (2002). Viral infection on phytoplankton–zooplankton system—a mathematical model. Ecological Modelling, 151(1), 15-28.
Misra, A. K. (2010). Modeling the depletion of dissolved oxygen in a lake due to submerged macrophytes. Nonlinear Analysis: Modelling and Control, 15(2), 185-198.
Marchettini, N., Mocenni, C., Vicino, A. (1999). Integrating slow and fast dynamics in a shallow water coastal lagoon. ANNALI DI CHIMICA-ROMA-, 89,505-514.
Allegretto, W., Mocenni, C., Vicino, A. (2005). Periodic solutions in modelling lagoon ecological interactions. Journal of mathematical biology, 51(4), 367-388.
Schaffer, G., Leth, O., Ulloa, O., Bendtsen, J., Daneri, G., Dellarossa, V.Sehlstedt, P. I. (2000). Warming and circulation change in the eastern South Pacific Ocean. Geophysical Research Letters, 27(9), 1247-1250.
Najjar, R. G., Pyke, C. R., Adams, M. B., Breitburg, D., Hershner, C., Kemp, M., ... Wood, R. (2010). Potential climate-change impacts on the Chesapeake Bay. Estuarine, Coastal and Shelf Science, 86(1), 1-20.
Najjar RGet al 2000 The potential impacts of climate change on the mid-atlantic coastal region Climate Research 14219-33
Jones RI 1977 The importance of temperature conditioning to the respiration of natural phytoplankton communities British Phycological Journal 12 277-85.
Medvinsky A B, Petrovskii SV, Tikhonova I A, Malchow H and Li B-L 2002 Spatiotemporal complexity of plankton and fish dynamics SIAM Rev. 44311-70.
Hoppe H-G, Gocke K, Koppe R and Begler C 2002 Bacterial growth and primary production along a north-south transect of the atlantic ocean Nature 416 168-71.
Franke U, Hutter K and J¨ohnk K 1999 A physical-biological coupled model for algal dynamics in lakes Bull. Math. Biol. 61 239-72.
Suthers, I. M., Richardson, A. J., & Rissik, D. (2019). The importance of plankton. Plankton: A Guide to Their Ecology and Monitoring for Water Quality, 1-13.
Glud, R. N. (2008). Oxygen dynamics of marine sediments. Marine Biology Research, 4(4), 243-289.
Regaudie‐de‐Gioux, A., & Duarte, C. M. (2012). Temperature dependence of planktonic metabolism in the ocean. Global Biogeochemical Cycles, 26(1).
Dusenge, M. E., Duarte, A. G., & Way, D. A. (2019). Plant carbon metabolism and climate change: elevated CO 2 and temperature impacts on photosynthesis, photorespiration and respiration. New Phytologist, 221(1), 32-49.
Correa-González, J. C., del Carmen Chávez-Parga, M., Cortés, J. A., & Pérez-Munguía, R. M. (2014). Photosynthesis, respiration and reaeration in a stream with complex dissolved oxygen pattern and temperature dependence. Ecological modelling, 273, 220-227.
Kumar, P., Erturk, V. S., Banerjee, R., Yavuz, M., & Govindaraj, V. (2021). Fractional modeling of plankton-oxygen dynamics under climate change by the application of a recent numerical algorithm. Physica Scripta, 96(12), 124044.
Hamid, M., Usman, M., Haq, R. U., & Tian, Z. (2021). A spectral approach to analyze the nonlinear oscillatory fractional-order differential equations. Chaos, Solitons & Fractals, 146, 110921.
Owolabi, K. M., & Atangana, A. (2017). Numerical approximation of nonlinear fractional parabolic differential equations with Caputo–Fabrizio derivative in Riemann–Liouville sense. Chaos, Solitons & Fractals, 99, 171-179.
Kumar, P., Erturk, V. S., Banerjee, R., Yavuz, M., & Govindaraj, V. (2021). Fractional modeling of plankton-oxygen dynamics under climate change by the application of a recent numerical algorithm. Physica Scripta, 96(12), 124044.
Sekerci, Y., & Ozarslan, R. (2020). Dynamic analysis of time fractional order oxygen in a plankton system. The European Physical Journal Plus, 135(1), 88.
Sekerci, Y., & Ozarslan, R. (2020). Oxygen-plankton model under the effect of global warming with nonsingular fractional order. Chaos, Solitons & Fractals, 132, 109532.
Nouri, K., Nazari, M., & Torkzadeh, L. (2020). Numerical approximation of the system of fractional differential equations with delay and its applications. The European Physical Journal Plus, 135, 1-14.