Evidence map›Paper›PMID 42884359›Full record

ArticleSensors (Basel, Switzerland)2026

A LoRa-Based Wireless Sensor Network for Urban Water Quality Monitoring with Real-Time Machine Learning and Energy Analysis.

Iman Valizadeh, Lorena Parra, Jaime Lloret

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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.

Iman ValizadehDepartment of Communications, Universitat Politècnica de València, 46022 Valencia, Spain.ORCID 0009-0002-9525-2800
Lorena ParraDepartamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0001-9215-8734
Jaime LloretDepartment of Communications, Universitat Politècnica de València, 46022 Valencia, Spain.ORCID 0000-0002-0862-0533

Funding

Ministerio de Ciencia e Innovación PID2023-153039OB-I00/AEI/10.13039/501100011033
6 · The paper itself

Abstract

This paper presents a LoRa-based system for real-time urban water quality monitoring. The proposed platform integrates several sensors, including ultraviolet (UV), infrared (IR), red-green-blue (RGB), pH, turbidity, and temperature sensors, to monitor different water quality parameters. A total of 1443 sensor records were collected during the experiments. Among these records, 75 UV measurements, corresponding to approximately 5.2% of the dataset, were missing or invalid while the Red, Green, and Blue (RGB) and IR measurements were available. Several regression and machine learning models were evaluated to reconstruct these UV values. Multiple Linear Regression showed limited performance in the initial random train-test evaluation (R

Indexed as

energy-efficient monitoringLoRa-based monitoringmachine learningRandom Foresturban water qualityUV reconstructionwireless sensor networks

Identifiers

PMID42884359
PMCPMC13667602

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.