Adaptive Integrated Land Management Framework (AILMF): A Data-Driven Model for Optimizing Land Uses in Iran's Arid and Semi-Arid Regions

Abstract: Sustainable land management in arid and semi-arid regions has emerged as one of the most pressing challenges of the twenty-first century, particularly in countries facing severe water scarcity, increasing anthropogenic pressures, and accelerating climate change impacts. Iran, where a substantial proportion of the territory is characterized by dryland ecosystems, requires innovative and adaptive approaches to optimize land-use allocation while preserving ecological integrity and supporting socio-economic development. This study introduces the Adaptive Integrated Land Management Framework (AILMF), a comprehensive data-driven model designed to optimize multiple land uses in Iran's arid and semi-arid regions. Unlike conventional land management approaches that primarily focus on one or two dimensions of land suitability, the proposed framework integrates ecological, environmental, socio-economic, and technological dimensions into a unified decision-support system. The framework consists of four interconnected layers: (1) a data acquisition layer integrating remote sensing, Geographic Information Systems (GIS), soil, climatic, and socio-economic datasets; (2) an analytical layer employing machine learning algorithms and ecological capacity assessments; (3) a multi-criteria optimization layer for land-use allocation and scenario simulation; and (4) a monitoring and feedback layer enabling continuous model updating and adaptive management. The principal contribution of this study lies in its interdisciplinary integration of spatial data science, machine learning, ecological resilience, and adaptive governance into a single operational framework.