<![CDATA[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.]]>
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