Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

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Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions

Hygiene and Environmental Health Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100114 - 100114

Published: Oct. 1, 2024

Language: Английский

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Artificial intelligence and water quality: From drinking water to wastewater

TrAC Trends in Analytical Chemistry, Journal Year: 2024, Volume and Issue: 172, P. 117597 - 117597

Published: Feb. 15, 2024

Language: Английский

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Artificial intelligence in microplastic detection and pollution control

Environmental Research, Journal Year: 2024, Volume and Issue: 262, P. 119812 - 119812

Published: Aug. 16, 2024

Language: Английский

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A critical analysis of parameter choices in water quality assessment

Water Research, Journal Year: 2024, Volume and Issue: 258, P. 121777 - 121777

Published: May 16, 2024

The determination of water quality heavily depends on the selection parameters recorded from samples for index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine parameter set four main reasons: reducing cost uncertainty, addressing eclipsing problem, enhancing performance predicting WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies this area. Such essential assess validity these objectives demonstrate effectiveness data-driven methods achieving goals. This paper sets out with two primary aims: first, provide review existing literature selecting parameters. Second, it seeks delineate evaluate principal motivations identified literature. manuscript categorizes into methodological groups refining parameters: one focuses preserving information within dataset, another ensures consistent prediction using full It characterizes each group evaluates how effectively approach meets predefined objectives. study presents minimal WQI approach, common both categories, only has successfully reduced recording costs. Nonetheless, notes simply number does not guarantee savings. Furthermore, classified as dataset demonstrated potential decrease whereas have been able mitigate issue. Additionally, since approaches still rely initial chosen by experts, they do eliminate need expert judgment. further points formula straightforward expedient tool assessing quality. Consequently, argues employing solely reduce enhance standalone solution. Rather, objective should be integrated more research critical analysis characterization lay groundwork future research. will enable subsequent proposed can achieve

Language: Английский

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Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 18, 2024

Abstract Identifying and measuring potential sources of pollution is essential for water management control. Using a range artificial intelligence models to analyze quality (WQ) one the most effective techniques estimating index (WQI). In this context, machine learning–based are introduced predict WQ factors Southeastern Black Sea Basin. The data comprising monthly samples different were collected 12 months at eight locations Türkiye region in Sea. traditional evaluation with WQI surface was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) pi-sigma neural networks (PS-ANNs) used WQI, accuracy proposed algorithms compared. SMN model PS-ANNs prediction modeling first time literature. According results obtained from ANN models, it found provide highly reliable approach that allows capturing nonlinear structure complex series thus generate more accurate predictions. analyses demonstrate applicability instead using other computational methods both particular resources general.

Language: Английский

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Comparative Analysis of Machine Learning Algorithms for Water Quality Prediction

Tellus A Dynamic Meteorology and Oceanography, Journal Year: 2024, Volume and Issue: 76(1), P. 177 - 192

Published: Jan. 1, 2024

Tellus A: Dynamic Meteorology and Oceanography is an open access journal focusing on all aspects of atmospheric dynamics related to Earth science processes. A, along with its sister B: Chemical Physical Meteorology, are international, peer-reviewed journals the International Meteorological Institute in Stockholm, independent not-for-profit body integrated into Department at Faculty Sciences Stockholm University, Sweden. The two serve international community researchers, policymakers, managers, media general public. Together they promote exchange knowledge about meteorology from across a range scientific sub-disciplines. Topics covered A include:dynamic | physical oceanography data assimilation techniques numerical weather prediction climate modelling observation. Types papers accepted include original research papers, review articles, brief notes, Letters Editor, special issues conference proceedings (from time time). operates single-blind peer-review policy. All published articles made freely permanently available online through gold publication CC BY license. Read Guidelines for Authors more information how submit your manuscript review.

Language: Английский

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AI and IoT: Supported Sixth Generation Sensing for Water Quality Assessment to Empower Sustainable Ecosystems

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

Language: Английский

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Developing a Semi-Automated Technique of Surface Water Quality Analysis Using GEE And Machine Learning: A Case Study for Sundarbans

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42404 - e42404

Published: Feb. 1, 2025

This study presents a semi-automated approach for assessing water quality in the Sundarbans, critical and vulnerable ecosystem, using machine learning (ML) models integrated with field remotely-sensed data. Key parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, pH-were predicted through ML algorithms interpolated Empirical Bayesian Kriging (EBK) model ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) AutoML, utilizing deep libraries to create dynamic, adaptive that enhance prediction accuracy. Comparative analyses showed ML-based effectively captured spatial temporal variations, aligning closely measurements. integration provides more efficient alternative traditional methods, which are resource-intensive less practical large-scale, remote areas. Our findings demonstrate this technique is valuable tool continuous monitoring, particularly ecologically sensitive areas limited accessibility. also offers significant applications climate resilience policy-making, as it enables timely identification of deteriorating trends may impact biodiversity ecosystem health. However, acknowledges limitations, including variability data availability inherent uncertainties predictions dynamic systems. Overall, research contributes advancement monitoring techniques, supporting sustainable environmental management practices Sundarbans against emerging challenges.

Language: Английский

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Data-driven water quality prediction using hybrid machine learning approaches for sustainable development goal 6

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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