Machine learning for sustainable land management: A focus on Italy.

Soil salinization poses a multifaceted challenge demanding a comprehensive approach combining environmental science, machine learning, geography, and socioeconomic analysis. Our study integrates these disciplines to unravel the complexities of soil salinization and devise effective mitigation strategies. We ground our investigation in understanding the geological and climatic fundamentals governing soil properties and processes, with a focus on the Mediterranean coastal areas. By harnessing the power of machine learning, we navigate the high-dimensionality and non-linearity of soil salinization, incorporating a comprehensive set of variables spanning geological, climatic, human activity, and socio-economic dimensions. Our models, trained on extensive datasets, are robust and capable of capturing intricate patterns associated with soil salinization. The Mediterranean coastal areas, with their unique ecological, climatic, and anthropogenic interactions, serve as a valuable case study for exploring the dynamics of soil salinization. Our approach integrates data on historical geological changes with current climatic and anthropogenic variables, creating a comprehensive model that encapsulates the temporal and spatial dimensions of soil salinization. This study aims to contribute meaningfully to global efforts in sustainable land management and environmental preservation.