A novel stochastic wastewater quality modeling based on fuzzy techniques.

Khadije Lotfi, Hossein Bonakdari, Isa Ebtehaj, Robert Delatolla, Ali Akbar Zinatizadeh, Bahram Gharabaghi

Abstract read

In one paragraph

Article in Journal of environmental health science & engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Quantitative microbial risk assessment of enteroviruses in raw-eatable vegetables irrigated by wastewater: examining different scenarios of washing.Journal of environmental health science & engineering · 2022
Article

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Khadije LotfiEnvironmental Research Center, Razi University, Kermanshah, Iran.

Hossein BonakdariDepartment of Soils and Agri-Food Engineering, Université Laval, Québec, G1V0A6 Canada.ORCID 0000-0001-6169-3654

Isa EbtehajEnvironmental Research Center, Razi University, Kermanshah, Iran.

Robert DelatollaDepartment of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5 Canada.

Ali Akbar ZinatizadehEnvironmental Research Center, Razi University, Kermanshah, Iran.

Bahram GharabaghiSchool of Engineering, University of Guelph, Guelph, Ontario NIG 2W1 Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM). These new pre-processing methodsare compared to previously employed non-linear approaches for modelling of wastewater influent/effluent 5-day biochemical oxygen demand (BOD

Indexed as

ANFIS-FCMFuzzy techniqueOutlier detectionStochastic modelWastewater quality parametersWater resources

Identifiers

PMID33312627

DOI10.1007/s40201-020-00530-8

PMCPMC7721937

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.