RESILIENCE-ORIENTED DECISION SUPPORT SYSTEM FOR ECOSYSTEM MANAGEMENT: INTEGRATING MACHINE LEARNING AND PARTICIPATORY MODELLING

Ecosystem management requires adaptive method which handles the complexity and uncertainty, especially the habitat degradation, biodiversity loss, and climate change. The existing methods of managing ecosystems often lack in the adaptability and necessary to precisely addresses the dynamic circumstances. To enhance the decision-making in ecosystem management, this study offers a Resilience-Oriented Decision Support System (RODSS) that integrates the Participatory Modelling (PM) and Machine Learning (ML) methods. Initially, the data are collected from the environmental sensors, satellite imagery, and stakeholder. Then, Bayesian Network (BN) algorithms are used for analysis and the System Dynamics (SD) modelling involves stakeholders in defining scenarios, assessing trade-offs, and co-developing strategies to improve the ecosystem resilience that determines the anomalies, and patterns in ecosystem health. The RODSS employs a Reinforcement Learning (RL) model for predicting the potential results and recommends the adaptive management techniques. The proposed system simulations are evaluated using the real-world case studies showcases the RODSS significantly enhances the accuracy and robustness of ecosystem manage ment techniques. The combinations of Machine Learning (ML) provide a processing of complex, high-dimensional data, while PM guarantees a contextual relevance and stakeholder engagement of the techniques, made possible for attaining more sustainable and effective.