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

Heliyon, Год журнала: 2024, Номер 10(6), С. e27920 - e27920

Опубликована: Март 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).

Язык: Английский

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Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Water, Год журнала: 2024, Номер 16(2), С. 264 - 264

Опубликована: Янв. 11, 2024

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the water index (IWQI), this research aims evaluate Naama, a region southwest Algeria. Hydrochemical parameters (cations, anions, pH, EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well geospatial representations were used determine groundwater’s suitability study area. In addition, efficient forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), K-Nearest Neighbours (KNN) models implemented. research, 166 samples calculate index. results showed that 42.18% them excellent quality, 34.34% very good 6.63% 9.64% satisfactory, 4.21% considered unsuitable irrigation. On other hand, indicate XGBoost excels accuracy stability, low RMSE (of 2.8272 high R 0.9834. SVR only four inputs (Ca2+, Mg2+, Na+, K) demonstrates notable predictive capability 2.6925 0.98738, while KNN showcases robust performance. distinctions between these have important implications making informed decisions agricultural management resource allocation within region.

Язык: Английский

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A review of the status, challenges, trends, and prospects of groundwater quality assessment in Nigeria: an evidence-based meta-analysis approach

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(15), С. 22284 - 22307

Опубликована: Фев. 29, 2024

Язык: Английский

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Evaluation of groundwater quality indices using multi-criteria decision-making techniques and a fuzzy logic model in an irrigated area

Groundwater for Sustainable Development, Год журнала: 2024, Номер 25, С. 101122 - 101122

Опубликована: Фев. 16, 2024

Язык: Английский

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Hydrological modeling for settling pond management in nickel mines: the Case of Hinatuan Mining Corporation in Tagana-an, Surigao del Norte, Philippines

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(2)

Опубликована: Янв. 20, 2025

Язык: Английский

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Irrigation suitability and health risk assessment of groundwater resources in the Firozabad industrial area of north-central India: An integrated indexical, statistical, and geospatial approach

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Март 15, 2023

The recent global upsurge in anthropogenic activities has resulted a decline the quality of water. This by extension increased ubiquity water pollution terms sources. application traditional assessment methods usually involves use conventional parameters and guideline values. may be associated with bias errors during computation various sub-indices. Hence, to overcome this limitation, it is critical have visual appraisal source human health risks exposure for sustainable resource management informed decision-making. Therefore, present study integrated multiple indices, spatio-temporal, statistical models assess suitability fifty groundwater samples (n = 50) within Firozabad industrial area irrigation drinking; as well likely from oral intake dermal contact inhabitants. Electrical conductivity (mean 1,576.6 μs/cm), total hardness 230.9 mg/L), dissolved sodium 305.1 mg/L) chloride 306.1 fluoride 1.52 occurred at concentrations above recommended standards; attributed influxes agricultural wastewater. index revealed that 100% are extremely polluted; was also supported joint multivariate analyses. majority irrigational indices (sodium adsorption ratio, Kelly’s Ratio, permeability index, percent sodium) long-term will result reduced crop yield unless remedial measures put place. Higher Hazard (HI > 1) nitrate ingestion recorded children population compared adult; an indication more predisposed Generally, risk levels appear increase western north-eastern parts area. From findings study, highly adequate practices, land use, treatment regulatory strategies place sustainability enhanced production protection.

Язык: Английский

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Assessment of hydrochemical characteristics, health risks and quality of groundwater for drinking and irrigation purposes in a mountainous region of Pakistan

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(31), С. 43967 - 43986

Опубликована: Июнь 25, 2024

Renowned for its agriculture, livestock, and mining, Zhob district, Pakistan, faces the urgent problem of declining groundwater quality due to natural human-induced factors. This deterioration poses significant challenges residents who rely on drinking, domestic, irrigation purposes. Therefore, this novel study aimed carry out a comprehensive assessment in considering various aspects such as hydrochemical characteristics, human health risks, suitability drinking While previous studies may have focused one or few these aspects, integrates multiple analyses provide holistic understanding situation region. Additionally, applies range common analysis methods (acid-base titration, flame atomic absorption spectrometry, ion chromatography), water index (WQI), indices, risk models, using 19 parameters. multi-method approach enhances robustness accuracy assessment, providing valuable insights decision-makers stakeholders. The results revealed that means majority parameters, pH (7.64), electrical conductivity (830.13 μScm

Язык: Английский

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Evaluation of surface water quality in Brahmani River Basin, Odisha (India), for drinking purposes using GIS-based WQIs, multivariate statistical techniques and semi-variogram models

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(12)

Опубликована: Ноя. 23, 2024

Язык: Английский

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Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

Язык: Английский

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Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

International Journal of Management Technology and Social Sciences, Год журнала: 2024, Номер unknown, С. 94 - 110

Опубликована: Май 20, 2024

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective management. Through a comprehensive review of the literature an in-depth analysis various AI ML techniques, this paper aims to put light cutting-edge approaches used in irrigation scheduling predictive modeling. The goal research is determine advantages, disadvantages, future directions ML-based management systems by means methodical algorithms, data sources, applications. Additionally, study seeks demonstrate how data-driven methods can enhance systems' sustainability, accuracy, precision. Stakeholders agriculture, resource management, conservation make well-informed decisions maximize techniques having thorough understanding theoretical underpinnings practical applications models. also attempts tackle issues like scalability, model interpretability, lack when implementing solutions for In final form, review's conclusions advance our use improve resilience efficiency, supporting adaptive sustainable strategies face rising scarcity concerns climate change. Design/Methodology/Approach: order gather information study, several articles from reliable sources were analyzed compared. Objective: To provide current gaps prediction models best suggest using fill these gaps. Results/ Findings: response growing challenges change, paper's findings highlight transformative potential optimizing scheduling, enhancing resilience, increasing strategies. Originality/Value: This uniqueness significance come its modeling ideal scheduling. It provides insights into new their possible optimization sustainability. Type Paper: Literature Review.

Язык: Английский

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