Failure mode effects and criticality analysis of water supply systems' risks: Path to water resources planning and policy
Bibliographic record
Abstract
Abstract The risk assessment of drinking‐water supply systems in Ogun State, Nigeria, was carried out using the failure mode effects and criticality analysis (FMECA) approach. The FMECA is a systemic process that identifies potentials failure modes within a system and was chosen for its causes and effect approach to assessing risks. The objective of the study was to assess drinking‐water supply systems and identify water supply systems' risks from source to point‐of‐use. Three major water supply sources were selected for assessment: hand‐dug wells, boreholes and public water supply sources. The sources were assessed by identifying the potential failure modes that exist within the water supply sources and the consequence of the identified risks on relevant stakeholders. The sources were divided into modules. The risk in each module was determined by multiplying failure rate (likelihood) and consequences of failure of the module. Risk reduction options include repair and maintenance measures, information dissemination on the procedures to reduce the identified risks and preventive and regulatory approaches. The resulting risks were characterized using FMECA risk matrix of each water source and classified into high, medium and low risks. Well cover and lining were the most risk‐prone modules for hand‐dug wells (high and medium risks). Broken well cover and lining serve as pathways to contaminants into the well. Casing and screen modules posed the highest risk for boreholes, recording high to medium risk. Cracked casing and broken screen provide access for contamination into boreholes. The module with the greatest risk for public water supply source was the point‐of‐abstraction/use module. Unsanitary containers and poor storage conditions is believed to be responsible for recontamination of the treated water Climate variability, environmental and anthropogenic influences were observed to be responsible for most of the identified risks. The study highlights that consumer participation is vital in ensuring the availability of safe drinking‐water, stressing consumer education as the most important channel. The study recommends the use of FMECA to ensure implementing preventive and regulatory measures by water monitoring agencies and for water resources planning and policy making.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".