Evidence map›Paper›PMID 38678400›Full record

ArticleWater science and technology : a journal of the International Association on Water Pollution Research2024

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Yong Wang, Biao Huang, David Z Zhu

Abstract read
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In one paragraph

Article in Water science and technology : a journal of the International Association on Water Pollution Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yong WangSchool of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China.
Biao HuangSchool of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China E-mail: huangbiao@nbu.edu.cn.ORCID https://orcid.org/0000-0001-9403-4508
David Z ZhuSchool of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China; Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada.

Funding

Key Research and Development Program of Zhejiang Province 2020C03082National Key R&D Program of China 2022YFC3203200Ningbo Young Technology Innovation Leading Talent Program 2023QL028
6 · The paper itself

Abstract

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

Indexed as

Machine LearningRainSewageModels, TheoreticalWater MovementsSewagelong short-term memorymachine learningrainfall-derived inflow and infiltrationrandom forestsewer flow prediction

Identifiers

PMID38678400

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.