Bayesian inference and prediction in Libya's emerging water-land-use complex
Keywords:
Bayesian inference, water resources, land use, sustainability, LibyaAbstract
This paper employs Bayesian statistical methods to model and predict the complex interactions between water resources and land use in Libya, a country facing significant environmental and planning challenges. The model integrates various data sources to assess current trends and project future scenarios under different policy and climate conditions. The results highlight critical pressure points and offer probabilistic predictions for sustainable resource management. This approach provides a robust framework for decision-makers to evaluate the long-term implications of land-use and water management policies in an uncertain environment.
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