A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination
Bibliographic record
Abstract
Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".