Parameter estimation and optimal control strategies for sustainable management of mangrove forests

Authors

Nurul Eliana Atika Mohd Idrus
Tun Hussein Onn University of Malaysia image/svg+xml
Sie Long Kek
Tun Hussein Onn University of Malaysia image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v14i3.7371
Download PDF
Published: 15-09-2026 • Issue: Vol. 14 No. 3: September 2026 • Section: Regular paper

Abstract

This paper presents an integrated computational framework combining parameter estimation and optimal control for sustainable mangrove forest management. We begin by addressing uncertainty in model parameters using a data-driven solution. A least-squares optimization problem is solved using the Levenberg-Marquardt algorithm to recursively estimate parameters in the dynamic mangrove ecosystem model. Later, we formulate an optimal control problem to drive mangrove populations toward ecological steady states as rapidly and efficiently as possible. To this end, we define the Hamiltonian function and derive the necessary optimality conditions to design optimal control policies. Our key contribution lies in unifying parameter estimation and control synthesis into a single, implementable algorithm tailored for mangrove dynamics. Numerical simulations demonstrate the algorithm’s efficacy: parameter estimates achieve high accuracy, as evidenced by low mean squared error, while the optimal control policy minimizes a defined performance index, ensuring ecological and operational efficiency. This integrated approach offers decision-makers a data-driven, mathematically rigorous tool to guide restoration and conservation efforts. By bridging estimation and control, our methodology enhances adaptive management of mangrove forests, supporting long-term ecological resilience and sustainability in dynamic coastal environments.
Keywords: Mangrove forest dynamics; Parameter estimation; Optimal control theory; Levenberg-Marquardt algorithm; Sustainable ecosystem management

References

[1] D. M. Alongi, “Carbon cycling and storage in mangrove forests,” Annual Review of Marine Science, vol. 6, pp. 195–219, 2014, doi: 10.1146/annurev-marine-010213-135020.

[2] E. B. Barbier, S. D. Hacker, C. Kennedy, E. W. Koch, A. C. Stier and B. R. Silliman, “The value of estuarine and coastal ecosystem services,” Ecological Monographs, vol. 81, no. 2, pp. 169–193, 2011, doi: doi.org/10.1890/10-1510.1.

[3] I. C. Feller, D. A. Friess, K. W. Krauss and R. R. Lewis, “The state of the world’s mangroves in the 21st century under climate change,” Hydrobiologia, vol. 803, no. 1, pp. 1-12, 2017, doi: 10.1007/s10750-017-3331-z.

[4] S. E. Hamilton and D. A. Friess, “Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012,” Nature Climate Change, vol. 8, no. 3, pp. 240–244, 2018, doi: 10.1038/s41558-018-0090-4.

[5] R. Sunkur, K. Kantamaneni, C. Bokhoree, U. Rathnayake and M. Fernando, “Modelling mangrove dynamics in Mauritius: Implications for conservation and climate resilience,” Journal for Nature Conservation, vol. 85, 126864, 2025, doi: 10.1016/j.jnc.2025.126864.

[6] B. A. Polidoro, K. E. Carpenter, L. Collins, N. C. Duke, A. M. Ellison, J. C. Ellison, and J. W. H. Yong, “The loss of species: Mangrove extinction risk and geographic areas of global concern.” PLoS ONE, vol. 5, no. 4, e10095, 2010, doi: 10.1371/journal.pone.0010095.

[7] A. Singh and S. Mahajan, “Mangrove forest parameter estimation using synthetic aperture radar data,” Annals of Plant and Soil Research, vol. 25, No. 3, pp. 501-505, 2023, doi: 10.47815/apsr.2023.10298.

[8] Y. Zheng and W. Takeuchi, “Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area,” Scientific Reports, vol. 12, no. 1, 2238, 2022, doi: 10.1038/s41598-022-06231-6.

[9] A. C. Kejeh, A. E. Gobo, S. A. Abere and R. E. Akpodee, “Modelling and numerical simulation of mangrove forest relative abundance in the presence of artisanal hydrocarbon refining,” International Journal of Environment Research and Earth Science, vol. 3, no. 4, 2024, https://cambridgeresearchpub.com/ijeres/article/view/148.

[10] S. Lenhart and J. T. Workman, Optimal Control Applied to Biological Models. Chapman and Hall/CRC, 2007.

[11] M. N. Hassan, M. H. A. Biswas and M. S. Uddin, “An ecological model for sustainable forest management of eco-system based on optimal control theory,” Journal of Nepal Mathematical Society, vol. 2, no. 1, pp. 19–44, 2019, doi: 10.3126/jnms.v2i1.33014.

[12] A. K. Paul, M. S. Khatun, M. H. A. Biswas, “Modelling and optimal control applied to reduce the effects of greenhouse gases emitted from the coal-based power plant in Bangladesh,” Heliyon, vol. 9, no. 8, e18409, 2023, doi: 10.1016/j.heliyon.2023.e18409.

[13] M. A. Sadi, A. Jamali, A. M. N. A. Kamaruddin and V. Y. S. Jun, “Cascade model predictive control for enhancing UAV quadcopter stability and energy efficiency in wind turbulent mangrove forest environment,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 10, 2024, doi: 10.1016/j.prime.2024.100836.

[14] Y. Zhang, B. Li, N. Yao, Q. Luan and P. Gu, “Study of Multi-Source River Discharge Data Assimilation in Collaboration with EKF and MIKE11HD.” Hydrological Sciences Journal, vol. 70, no 9, pp. 1554–69, 2025, doi:10.1080/02626667.2025.2492884.

[15] P. F. Hinkel and D. B. Pourkargar, ”An integrated moving horizon estimation and model predictive control framework for semiclosed greenhouse systems,” 2022 IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, 2022, pp. 699-705, doi: 10.1109/CCTA49430.2022.9966060.

[16] D. Karimanzira and T. Rauschenbach, ”Moving horizon vs. unscented Kalman filter for state estimation in streamflow prediction,” World Journal of Environmental Engineering, vol. 7, no. 1, pp. 1-10, 2022.

[17] G. Grisetti, T. Guadagnino, I. Aloise, M. Colosi, B. Della Corte and D. Schlegel, ”Least squares optimization: from theory to practice,” Robotics, vol. 9, no. 3, 2020, doi: 10.3390/robotics9030051

[18] S. M. Dunn, P. V. Moghe and A. Constantinides, Numerical Modeling in Biomedical Engineering, Academic Press.

[19] Eiman, A. Khan, M. Sha and M. Khan, ”Taylor series expansion method to compute approximate solution for nonlinear dynamical system,” Journal of Fractional Calculus and Nonlinear Systems , vol. 3, no. 1, 2022, doi:10.48185/jfcns.v3i1.501.

[20] M. Bierlaire, Optimization: Principles and Algorithms. 2 Ed, EPFL Press.

[21] A, Khamis, Optimization Algorithms. Manning Publication, 2024.

[22] J. Yong, Optimization Theory: A Concise Introduction. World Scientific Connect, 2018.

[23] S. A. Frank, Control Theory Tutorial: Basic Concepts Illustrated by Software Examples. Springer, 2010.

[24] K. Zhou, J. C. Doyle and K. Glover, Robust and Optimal Control, PrenticeHall, Englewood Cliffs, NJ, 1996.

[25] F. L. Lewis, D. Vrabie and V. L, Syrmos, Optimal Control. 3rd Ed, John Wiley & Sons, 2012.

[26] K. L. Teo, B. Li, C. Yu and V. Rehbock, Applied and Computational Optimal Control: A Control Parametrization Approch, Springer Cham, 2021.

[27] K. Atkinson, W. Han, D. Stewart, Numerical Solution of Ordinary Differential Equations, John Wiley & Sons, 2009.

[28] P. St¨adter, Y. Sch¨alte, L. Schmiester, J. Hasenauer and P. L. Stabpao, ”Benchmarking of numerical integration methods for ODE models of biological systems,” Science Reports, vol. 11, 2696, 2021, doi: 10.1038/s41598-021-82196-2

How to Cite

[1]
N. E. A. Mohd Idrus and S. L. Kek, “Parameter estimation and optimal control strategies for sustainable management of mangrove forests”, IJEEI, vol. 14, no. 3, pp. 796–807, Sep. 2026, doi: 10.52549/ijeei.v14i3.7371.