Sustainable solutions with AHP, reliability, and HAN-fuzzy sensitivity analysis for landfills in Saudi Arabia
Bibliographic record
Abstract
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. These 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. • A novel integration of AHP and HAN-Fuzzy models is proposed to assess landfill sustainability in arid and hot climates. • The study identifies Resource Reservoir, DS & M&O costs, and Site Selection as the most sensitive variables influencing landfill performance. • A comprehensive sensitivity analysis was conducted using real data and expert-derived weights across five decision criteria. • Outputs are linked to Saudi Vision 2030 goals, supporting policy on sustainable waste management and circular economy initiatives. • First study to apply HAN-Fuzzy for feature selection and sensitivity analysis in landfill evaluation within the Saudi Arabian context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".