Sustainable solutions with AHP, reliability, and HAN-fuzzy sensitivity analysis for landfills in Saudi Arabia

Landfills in arid and hot regions pose unique challenges due to accelerated decomposition rates and heightened risks of environmental contamination. This study explores the processes, treatment methods, and design considerations critical for managing waste in such extreme environments. Focusing on Saudi Arabia as a case study, the analysis highlights the need for climate-specific solutions to improve the design and operational efficiency of landfills. To identify key sustainability drivers, a hybrid sensitivity framework combining the Analytic Hierarchy Process (AHP) and a Hybrid Adaptive Neuro-Fuzzy Inference System (HAN-Fuzzy) was employed. AHP-derived weights ranged from 0.07 to 0.43, reflecting expert prioritization of variables such as resource reservoir (RR), design, construction & maintenance costs (DS and M&O), and site selection (SS). In contrast, HAN-Fuzzy revealed that RR was the most influential variable (RMSE = 3.29 × 10⁻⁶), followed by DS and M&O (RMSE = 2.20 × 10⁻⁵) and SS (RMSE = 3.28 × 10⁻⁵), illustrating a notable divergence between expert perception and data-driven impact. The findings underscore the importance of aligning strategic planning with both stakeholder input and empirical sensitivity outputs. The study offers actionable insights for policymakers, landfill operators, and environmental engineers seeking to optimize waste management in arid regions. Future directions include incorporating predictive modeling, advanced biodegradation technologies, and stakeholder engagement frameworks, all in alignment with Saudi Arabia’s Vision 2030 goals for sustainable resource use and environmental resilience.