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REVIEW 4 major objections 5 minor 28 references

Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A wastewater treatment plant's high-dimensional monitoring history can be compressed into a small set of explained operation modes, giving managers a low-dimensional view of the plant's current state.

desk verdict A serious applied case study with real plant data, but the SHAP explanation step is never defined for PCA/DBSCAN outputs, making the central explainability claim unsupported until fixed. read the letter →

arxiv 2506.05958 v1 pith:434PZN6E submitted 2025-06-06 eess.SY cs.LGcs.SY

Alicia Beneyto-Rodriguez , Gregorio I. Sainz-Palmero , Marta Galende-Hernández , María J. Fuente , José M. Cuenca This is my paper
classification eess.SYcs.LGcs.SY
keywords WWTPoperationmodeseXplainableArtificialIntelligenceSHAPDBSCANPCAknowledgeextractiondimensionalityreduction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a methodology for turning years of high-dimensional wastewater treatment plant (WWTP) monitoring data into a handful of explainable operation modes. Using 15 months of real SCADA data from the AQUAVALL WWTP, it reduces 3,231 monitored variables to 453 usable ones, then to 7 principal components, clusters the data with DBSCAN, and explains each cluster with SHAP values. The result is a low-dimensional space in which each new sample can be located at a glance, with 15 operation modes found at 12-hour resampling and 13 at 24-hour resampling. The detected modes match plant records and expert knowledge—regular operation, maintenance shutdowns, and heavy-rain episodes—so the unsupervised clusters are claimed to reflect real plant states rather than arbitrary groupings.

What carries the argument

The pipeline is the machinery: expert-guided cleaning and resampling (12h and 24h) reduce the raw SCADA stream, PCA projects the remaining 453 standardized variables onto seven principal components that retain most of the variance, DBSCAN with MinPoints=1 and an epsilon chosen from the k-distance graph discovers dense regions as operation modes, and KernelSHAP attributes each mode and principal component to the original plant variables and facility units. The work of the pipeline is to turn an unlabeled, high-dimensional time series into a labeled, low-dimensional map of plant states expressed in domain terms.

What would settle it

Re-run the SHAP explanation step with two different explicit surrogate models, for example a classifier that predicts DBSCAN cluster labels from the original variables and a regressor that predicts each principal-component score, and compare the top-ranked variables for each mode; if materially different rankings emerge, the mode explanations are not uniquely determined by the data.

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Extended reading notes

Core claim

The central claim is that a large historical monitoring database can be summarized as a small set of explained operation modes, each represented by a point in a reduced variable space, and that this summary is usable in real time. The paper demonstrates this on the AQUAVALL WWTP: after removing irrelevant and low-quality variables, PCA keeps seven components that preserve about 58–60% of the variance; DBSCAN (with MinPoints=1 so that rare events are kept as modes rather than noise) finds 15 modes at 12-hour resampling and 13 at 24-hour resampling; SHAP attributes each mode to the most influential plant variables and units. The authors validate the modes against the plant's logged events and expert knowledge, identifying regular operation, maintenance and power-line shutdowns, and rain-driven episodes among the found modes.

Load-bearing premise

The explanation step assumes Shapley-value attributions (SHAP) can meaningfully explain principal components and clusters, but the paper never specifies the predictive model whose output those values attribute, so the variable rankings rest on an unstated surrogate.

Editorial extensions

If this is right

  • Plant managers can see the current operating state as a single point in a seven-dimensional space and its distance to known modes, without reading thousands of SCADA variables.
  • New or unknown samples that fall outside the known modes can be flagged immediately and explained in terms of the variables and units involved, as was done for the power-line and connection-failure online samples.
  • The 12-hour resampling preserves short-term fluctuations and transient states, while the 24-hour resampling yields more stable modes but can hide rapid changes, so the choice of resampling window changes which modes are visible.
  • When the model is rebuilt with 15 months of data, samples that were previously exceptional can be absorbed into the regular operation mode, showing that incremental updating tracks the plant's evolution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper does not define the prediction function behind its SHAP values, a direct test is to re-run the attributions with an explicit surrogate model, such as a cluster-label classifier or a PC-score regressor, and see whether the mode rankings survive.
  • The same pipeline should transfer to other wastewater or drinking-water utilities that keep SCADA histories, provided the expert-knowledge validation step is repeated locally.
  • A practical extension is to turn the reduced-space distance to the regular operation mode into a live anomaly score, alerting managers when a new sample falls far from any known mode.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes an XAI-based unsupervised methodology to identify and explain operation modes of a wastewater treatment plant (WWTP) from SCADA logs. The pipeline consists of data cleaning, resampling at 12-hour and 24-hour intervals, standardization, PCA-based dimensionality reduction to seven principal components, DBSCAN clustering with MinPoints=1 and elbow-selected epsilon values, and KernelSHAP to attribute mode membership to original plant variables. The method is applied to 12 months of AQUAVALL WWTP data for the offline stage and 3 months for an online stage, reporting 15 operation modes for the 12-hour resampling and 13 for the 24-hour resampling, with qualitative expert validation linking several modes to shutdowns, rain episodes, and connection failures.

Significance. If the pipeline is valid, the paper offers a practical tool for plant operators: it compresses thousands of monitored variables into a small set of operation modes and provides per-mode variable attributions, with an online stage that could support real-time monitoring. Strengths of the work include the use of a real industrial dataset, the explicit offline/online split with three months of unseen data, and the effort to validate clusters against plant records and expert knowledge. However, the central explainability step is not formally well-defined, several parameter choices are justified post hoc on the same data used for clustering, and the validation is largely qualitative; as a result, the scientific claims are currently not established at the level expected for a journal publication.

major comments (4)
  1. [§2.3, Eq. (8); §3.1; §4.2.1] Equation (8) defines Shapley values only for a specified predictive model f, but the manuscript never defines f for the PCA scores or the DBSCAN cluster labels. The text states that KernelSHAP is used to explain PCA and clustering outputs, yet for a clustering assignment no real-valued prediction function f(S) is given; without such a function the SHAP values in Tables 4–9 are not well-defined. Please specify the model or surrogate (e.g., a classifier trained to reproduce cluster membership, or a reconstruction loss for PCA) whose predictions are explained, and re-derive or recompute the attributions accordingly.
  2. [§2.2, §4.1.1, §4.2] With MinPoints=1, every point satisfies the core-point condition in Eq. (6), so DBSCAN degenerates into connected components of the ε-neighborhood graph and the density-based distinction between core, border, and noise points is lost. The k-distance elbow rule is then applied with k=MinPoints=1, which reduces to nearest-neighbor distances rather than a density estimate. Since the choices ε=1.3 (12 h) and ε=1.1 (24 h) are justified only by this elbow heuristic on the training data, please provide a sensitivity analysis over ε and MinPoints and state explicitly what density property, if any, the resulting clusters are intended to preserve.
  3. [§4.2, §4.2.1] The PCA projection and the DBSCAN parameters are fitted on the same 12 months that are then clustered, so the discovered modes are partly in-sample summaries. The online stage provides some independent evidence, but the evaluation is qualitative only, matching three and two new modes to plant records. Please add quantitative validation, such as cluster stability measures, silhouette or Davies–Bouldin indices, and out-of-sample assignment error on the 3-month held-out data, and report how sensitive the number and composition of modes are to the PCA rank and the ε choices.
  4. [§4.2.1, Tables 4–9] The expert validation is not systematic: statements such as "seem to be" or "match with" are not supported by a defined matching protocol, and only a subset of the discovered modes is explicitly linked to recorded events. Please specify the criteria used to match modes to expert records, report the number of modes checked, and ideally use a blinded or pre-registered comparison to support the claim that the clusters reflect real operation modes.
minor comments (5)
  1. [General] There are several typographical errors that should be corrected: "datset" near Eq. (5), "outlayers" in Section 3.1, "matching learning" in Section 4.2, "variable space space" in Section 4.2.1, and "apperance" in the online 12-hour case.
  2. [§2.3, Eq. (8)] The notation f(S) is overloaded: it denotes a model prediction in Eq. (8), but later the same symbol is used for explaining PCA and cluster outputs. Please use separate symbols, for example h for the function being explained, to avoid ambiguity.
  3. [§4] The sentence describing the maximum flow rate of the WWTP is incomplete: "able to manage a maximum flow rate up to m3 seg" lacks the numerical value and unit. Please provide the missing quantity.
  4. [References] The reference list contains formatting and typographical errors, such as "Phillosphical Transaction" in [16], inconsistent journal-name styling in [1], and the very long reference [11]; please unify the reference style and correct the typos.
  5. [§4.2.1, Figures 6, 8, 10, 12] The figures plot only three principal components at a time, without explaining why those particular triples were chosen or whether the remaining components affect the visual interpretation of mode separation. Please add a brief explanation of the component-selection rule for the plots.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the pipeline is descriptive and the online stage is genuinely out-of-sample.

full rationale

The paper does not derive a numerical prediction from a fitted parameter that was itself fitted to the target quantity. The operation modes are DBSCAN clusters of the offline training data, and the online stage assigns new samples to those previously discovered modes using the PCA transform and incremental DBSCAN; this is a standard out-of-sample application rather than a prediction that reduces to the fit by construction. No self-citation is load-bearing: the SHAP references are external. The main technical weakness is that KernelSHAP is used to explain PCA and DBSCAN outputs without explicitly defining the predictive model f required by Eq. (8); this is a correctness and reproducibility gap, but it is not circular, because the paper does not claim that the SHAP values are derived from a model whose output is the same quantity being explained. The use of expert knowledge both for preprocessing and for validation is a consistency check, not a formal circular reduction. Overall, the central claim is an unsupervised summary of historical data, and no equation in the paper is shown to be equivalent to its own input by construction.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim relies on several hand-chosen parameters and unstated assumptions. The most consequential are the SHAP-as-explainer-for-clustering premise and the MinPoints=1 setting, which changes DBSCAN's semantics. No new physical entities are introduced.

free parameters (6)
  • DBSCAN epsilon for Ts=12h offline = 1.3
    Selected via k-distance graph elbow on the same 12-month training data; no sensitivity analysis is provided.
  • DBSCAN epsilon for Ts=24h offline = 1.1
    Selected via k-distance graph elbow on the same 12-month training data; no sensitivity analysis is provided.
  • DBSCAN MinPoints = 1
    Chosen by the authors to avoid labeling rare events as noise; this reduces DBSCAN to epsilon-neighborhood connected components.
  • Number of principal components = 7
    Selected by the elbow of the variance plot; retains 58.04% (Ts=12h) and 60.51% (Ts=24h) of variance, directly affecting all downstream clustering.
  • Resampling window = 12h and 24h
    Two windows selected by domain expertise; results differ between them, showing sensitivity to this choice.
  • Missing-value removal threshold = 1%
    Variables with more than 1% missing values were removed; this threshold influences the final 453-variable set.
assumptions (4)
  • domain assumption Retaining seven principal components preserves enough information for the operation-mode task.
    The elbow criterion is a heuristic, not a proof that the retained 58-60% variance is sufficient for clustering.
  • ad hoc to paper SHAP values can be computed for PCA and DBSCAN outputs without specifying a predictive function f.
    Eq. (8) defines SHAP for a model f(S); the paper never defines f for principal components or cluster assignments, so the explanations rely on an unstated surrogate.
  • ad hoc to paper MinPoints=1 preserves the density-based semantics of DBSCAN.
    With MinPoints=1, DBSCAN reduces to connected components of the epsilon-neighborhood graph; the paper itself notes increased sensitivity to noise.
  • domain assumption Expert knowledge of AQUAVALL is a sufficient validation source for the unsupervised modes.
    Validation in Sections 4.2.1 and 5 is based on qualitative matching to plant records; no protocol, inter-rater agreement, or quantitative metric is given.

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Pith. "Pith review of Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP." pith.science (2026). https://pith.science/paper/434PZN6E

@misc{pith2026250605958,
  author       = {Pith},
  title        = {Pith review of: Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/434PZN6E}},
  note         = {Machine review of arXiv:2506.05958}
}
read the original abstract

Water reuse is a key point when fresh water is a commodity in ever greater demand, but which is also becoming ever more available. Furthermore, the return of clean water to its natural environment is also mandatory. Therefore, wastewater treatment plants (WWTPs) are essential in any policy focused on these serious challenges. WWTPs are complex facilities which need to operate at their best to achieve their goals. Nowadays, they are largely monitored, generating large databases of historical data concerning their functioning over time. All this implies a large amount of embedded information which is not usually easy for plant managers to assimilate, correlate and understand; in other words, for them to know the global operation of the plant at any given time. At this point, the intelligent and Machine Learning (ML) approaches can give support for that need, managing all the data and translating them into manageable, interpretable and explainable knowledge about how the WWTP plant is operating at a glance. Here, an eXplainable Artificial Intelligence (XAI) based methodology is proposed and tested for a real WWTP, in order to extract explainable service knowledge concerning the operation modes of the WWTP managed by AQUAVALL, which is the public service in charge of the integral water cycle in the City Council of Valladolid (Castilla y Le\'on, Spain). By applying well-known approaches of XAI and ML focused on the challenge of WWTP, it has been possible to summarize a large number of historical databases through a few explained operation modes of the plant in a low-dimensional data space, showing the variables and facility units involved in each case.

Figures

Figures reproduced from arXiv: 2506.05958 by the authors.

Figure 1
Figure 1. Target overview. with environmental regulations. However, due to the complexity of the nature of WWTP processes, traditional analytical approaches can often fall short in interpreting these operation modes. On the other hand, these facilities are monitored in large scale supplying large databases about plant operation, which implies a need for a data summary to simply explain the WWTP operation mode and which can be… view at source ↗
Figure 2
Figure 2. Methodology scheme. must be addressed. Finally, the last part of the preprocessing involves the standardization of each of the acquired variables. Here, the z-score formulation is used for this goal. Notwithstanding the above, the most relevant challenge to address when a data driven approach is involved is the data dimensionality, which is usually done through variable selection or extraction to manage the curse of… view at source ↗
Figure 3
Figure 3. Water cycle at AQUAVALL - Valladolid (www.aquavall.es). [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: AQUAVALL WWTP - Valladolid (www.aquavall.es). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Relevance of the Principal Components by PCA. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Operation Modes based on DBSCAN (ε = 1.3 and the M inP oints = 1). (a) Operation Modes by months (P C1,P C2 and P C3). (b) Operation Modes by months (P C5, P C6 and P C7) [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Operation Modes based on months (ε = 1.3 and the M inP oints = 1). But all this has to be explained in terms of the WWTP domain, and therefore according to the variables managed by the WWTP managers. Tables 4 and 5 show an extract containing the variables of the facili…
Figure 8
Figure 8. Figure 8: Online WWTP Operation Model (Ts = 12hours) WWTP Relevant Variables for New Unknown online modes 1 2 3 FC4003.Q Max L1S4008.est L1S4008.est L1S4008.est FC4002.Q Max FC4001.Q Max FC4002.Q Max FC4001.Q Max FC4003.Q Max FC4001.Q Max FC4004.Q Max FC4004.Q Max FC4004.Q Max F…
Figure 9
Figure 9. Figure 9: Updated WWTP Operation Mode model. • Case: Ts = 24hours, Offline Here, a similar analysis has been made for the sample time Ts = 24hours. Once more, the parameter M inP oints was selected as only 1 point in order to allow a better detection of minimal clusters, due to …
Figure 10
Figure 10. Figure 10: Operation Modes based on DBSCAN (ε = 1.1 and the M inP oints = 1). (a) Operation Modes by months (P C1,P C2 and P C3). (b) Operation Modes by months (P C3, P C5 and P C6) [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Operation Modes based on months (ε = 1.1 and the M inP oints = 1) The explanation of these modes is newly obtained by SHAP, and shown in Tables 7 and 8. The cell colour is associated with the WWTP unit for each variable. All this allows us to know the operation modes …
Figure 12
Figure 12. Figure 12: Online WWTP Operation Model (Ts = 24hours) If the WWTP operation modes model is in need of an update, this is carried out by the offline steps of this proposal [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Updated WWTP Operation Mode model. modes. On the other hand, the results revealed some slight differences between the two resampling times: the 12 hour sample time captured short-term variations in WWTP operations. The detailed patterns obtained are valuable for detec…

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