Evidence map›Paper›PMID 39840274›Full record

ArticleWater research X2025

Laboratory performance assessment of low-cost water level sensor for field monitoring in the tropics.

Ning Ding, Qingchuan Zhu, Frederic Cherqui, Nicolas Walcker, Jean-Luc Bertrand-Krajewski, Perrine Hamel

Abstract read
In one paragraph

Article in Water research X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

6 authors.

Ning DingAsian School of the Environment and Earth Observatory of Singapore, Nanyang Technological University, Singapore.
Qingchuan ZhuINSA Lyon, DEEP EA 7429, F-69621, Villeurbanne cedex, France.
Frederic CherquiINSA Lyon, DEEP EA 7429, F-69621, Villeurbanne cedex, France.
Nicolas WalckerINSA Lyon, DEEP EA 7429, F-69621, Villeurbanne cedex, France.
Jean-Luc Bertrand-KrajewskiINSA Lyon, DEEP EA 7429, F-69621, Villeurbanne cedex, France.
Perrine HamelAsian School of the Environment and Earth Observatory of Singapore, Nanyang Technological University, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As Water Sensitive Urban Design (WSUD) is a key strategy in integrated urban water management worldwide, there is a need for robust monitoring of WSUD systems. Being economical and flexible for operation and communication, low-cost sensor systems show great potential to mainstream digital water management. Yet, such systems are insufficiently tested, casting doubt on the reliability of their measurements. Here, we document a robust testing approach for a pressure transducer water level low-cost sensor (KIT0139) and a traditional sensor (OTT PLS) in both laboratory and field conditions. We tested six different devices under three temperatures relevant to tropical climate: 25, 30, 35 °C and proposed a field calibration approach. Results reveal that the low-cost sensors were robust as the six individual devices performed consistently under different testing conditions. After calibration, low-cost sensors provided sufficient accuracy (±10mm) and precision for water levels more than 0.05m. While varying water flow direction did not significantly influence the performance, we showed that calibration should be done for individual devices. In addition, large (>5 °C) variations in water temperature and varying wet/dry conditions may also influence the performance of the low-cost sensors. The field calibration approach was validated in a 3-month experiment, confirming that this model of low-cost sensor can effectively replace traditional sensors in the field in tropical climates. Our study confirms that systematic and thorough testing is needed for low-cost sensors systems to realize their full potential for scientific-grade applications. We provide practical recommendations to conduct such testing from the laboratory to the field.

Indexed as

Low-cost sensorMeasurement performanceMonitoringPressure transducerWater level

Identifiers

PMID39840274
PMCPMC11745960

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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.