Defining low-conflict areas in Marsaxlokk : an AI-driven GIS approach

Abstract

Coastal regions like Marsaxlokk, Malta, face intensifying spatial conflicts as traditional livelihoods, industrial expansion, and environmental preservation converge within limited geographic boundaries. This study develops and tests a hybrid Geographic Information Systems and Machine Learning framework to identify low-conflict zones in Marsaxlokk Bay, supporting more sustainable marine spatial planning. Seventeen spatial layers representing commercial, environmental, aquaculture, and tourism activities were processed, normalized, and combined using raster-based composite mapping and weighted overlays to visualize spatial pressures. A Genetic Algorithm was employed to optimize layer weightings, identifying zones that balance environmental sensitivity with operational feasibility. Results demonstrate that low-conflict areas vary significantly under different stakeholder priorities, with the GA revealing an optimal location that prioritizes low storm turbidity and restricted anchoring zones. The approach offers a replicable, cost-effective alternative to prohibitively expensive marine planning tools by leveraging open-source data and accessible computational methods. This research underscores the potential of AI-driven GIS workflows to enhance coastal planning, supporting data-informed, inclusive, and adaptive decision-making in environments characterized by complex spatial claims.