A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments
Bibliographic record
Abstract
• A fuzzy logic evaluation model is established to deal with the uncertainty. • The control rules are set up to reflect the influence of relationships. • A sensitivity analysis was conducted on the indicators of the evaluation system. • The water security of MRB is at a safe status and faces various challenges. Quantitative and qualitative assessments are crucial means to understand the status of water security. In this paper, an index system for evaluating and diagnosing water security in the Mekong River Basin (MRB) was constructed based on the DPSIR model. A fuzzy logic evaluation model is established to deal with the nonlinearity and uncertainty of the evaluation index. In addition, the control rules of fuzzy reasoning are set up to reflect the influence relationship between evaluation indexes. Furthermore, the sensitivity of the evaluation method is reported and ways for water security improvement are proposed. The main findings are: (1) The water security score of MRB is 65.034 which is considered to be a safe status. China, Laos, and Thailand are water secure, while Myanmar, Cambodia, and Vietnam are facing water security problems. (2) The growth of Driving forces (D) and Impacts (I) are related to the improvement of water security. Pressures (P) have a significant threshold effect on water security. States (S) improvement has a direct and sensitive promotional effect. Responses (R) and water security are positively correlated. (3) The driving force has the strongest positive impact on water security in Laos, while it has a negative impact on Vietnam. Pressures has a minimal impact on China’s water security, while Cambodia faces a larger impact from Pressures. The influence of the State on the water security of basin countries is relatively stable, but in Myanmar, it has a negative impact. The effects of environmental changes hurt China’s water security. Vietnam has room to reduce these effects and enhance water security. The response dimension significantly improves the water security of the riparian countries but has a lesser impact on Cambodia and Myanmar. This research underscores the superiority of fuzzy logic in addressing multi-indicator, non-linear, and complex issues. It provides a flexible framework that accommodates the inherent uncertainties and complexities of water security assessments, captures dynamic interactions between various components of water security, and reveals non-linear relationships and threshold effects that conventional methods might overlook.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".