A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia

The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and VORS to predict health conditions in Abha city of Saudi Arabia from the past future. In this study, support vector machine (SVM) classifier was utilized classify land use cover (LULC) maps for 1990, 2000, 2018. LULCs dynamics 1990–2000, 2000–2018, 1990–2018 were computed using delta (Δ) change Markovian transitional probability matrix. future LULC map 2028 predicted artificial neural network-cellular automata (ANN-CA). learning algorithms, such as random forest (RF), classification regression tree (CART), distribution function (PDF) perform sensitivity analysis. Pearson’s correlation technique used explore between models their driving variables. 1990–2028 inference system with model. results showed that urban areas increased 334.4% 1990 Except dense vegetation, all natural resources generated services have been decreased significantly due rapid continuous urbanization process. A (2028) built-up area would be 343.72 km2. new 169 All techniques analysis proximity areas, scrubland are highly sensitive suitability simulate 2018 2028. Global fragmentation or organization most parameter conditions.