Bayesian inference and prediction in Libya's emerging water-land-use complex

By: A. Ramali , G. Holloway

Libyan authority for scientific research

Issue: Vol 22 |First Issue | 2016

article language: English

Abstract:

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.