Insights into the application of explainable artificial intelligence for biological wastewater treatment plants: Updates and perspectives
Bibliographic record
Abstract
Explainable artificial intelligence (XAI) is an interactive platform that assists users in comprehending the decisions and predictions made by machine learning (ML) models. This allows users to enhance their knowledge of ML models and their functioning, which not only helps in mitigating bias and errors but also aids in improving user decision-making confidence. XAI, due to its ability to increase the model output interpretation, has gained significant attention in biological wastewater treatment plants (WWTPs). This is owing, in particular, to the fact that it facilitates the experts in steering knowledge about the predictions and decisions made by ML, thus guaranteeing that the model decisions are fair and unbiased. ML has made amazing advances in recent years, thanks to its exponential growth in possessing the power to process massive volumes of data, allowing it to be widely embraced in WWTPs. This review seeks to illustrate the potential of XAI for WWTP applications such as process modeling and control, soft sensing, fusion of data, and the internet of things, and fill the knowledge gap by thoroughly introducing XAI techniques and their use in smart wastewater engineering. Overall, the features of XAI can aid in establishing reliable and efficient water resource management , which is quintessential to achieving environmental sustainability . It is envisioned that the prospects offered would spark new lines of study, helping to reduce the current skepticism and apprehension about ML adoption and integration in WWTP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".