Enhancing the Implementation of Nature-Based Solutions in Coastal Management: A Integrated Approach of Agent-Based Modeling, Social Network Analysis, and Machine Learning (Case Study: Pangandaran Village, Indonesia) Atik Nurul Aini (a*)
(a) Cerdas Antisipasi Risiko bencana Indonesia (CARI!), Jalan Sepak Bola 5, Bandung 40293, Indonesia
Abstract
This study proposes an innovative approach to enhance the implementation of Nature-Based Solutions (NBS) within the context of coastal management. The approach integrates the methodologies of Agent-Based Modeling (ABM), Social Network Analysis (SNA), and Machine Learning (ML). The primary objective of this research is to address collaboration constraints that frequently impede the successful execution of NBS by expanding the network of interactions among diverse decision-making agents. The research centers around Pangandaran Village as a case study, which grapples with complex coastal management challenges. The amalgamation of ABM, SNA, and ML enables an in-depth analysis of the relationships between various decision-making entities relevant to NBS implementation. The outcomes of the social network analysis unveil potential collaboration barriers, while the ABM model offers insights into how enhanced interactions can overcome these barriers. The integration of Machine Learning enriches the analysis by identifying patterns within data and aiding in the anticipation of decision-making agent behaviors across various scenarios. The Machine Learning model informs more effective interventions and supports more accurate decision-making concerning NBS implementation. The results of this research provide practical and sustainable guidance for designing and implementing NBS in Pangandaran Village. Through this unique fusion of ABM, SNA, and ML, the study contributes to more effective, sustainable, and dynamically adaptable coastal management efforts.
