Water Quality Monitoring Using IoT &amp; Machine Learning

Omambia, Andrew; Maake, Benard Magara; Wambua, Anthony

doi:10.23919/ist-africa56635.2022.9845590

Cited by 12 publications

(3 citation statements)

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“…The greater precision of the model is illustrated by its ability to predict chlorine material far better than present techniques. The aim of this paper [2] is to access to clean water is seen as a basic right of human and is necessary for human survival. As water is used by people, mostly from springs and pipes in nearby of towns, contamination, leaks, and loss occur.…”

Section: IImentioning

confidence: 99%

Monitoring of Water Quality using Machine Learning - A Review

Amith,

et al. 2024

IJRASET

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The incorporation of machine learning (ML) can improve the monitoring of water quality. It improves the sustainability and security of water resources by enabling forecasting and early detection of possible water contamination. A real-time system that collects information on numerous water parameters, including pH level, dissolved oxygen, turbidity, temperature, and conductivity, is made possible by the combination of IoT technology with ML algorithms. The system models complex links between water quality parameters and looks for variations from typical patterns using a mix of supervised and unsupervised learning and anomaly detection methods. Advantage of this method include cost-effectiveness, scalability, remote accessible, and real-time monitoring

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Section: IImentioning

confidence: 99%

Monitoring of Water Quality using Machine Learning - A Review

Amith,

et al. 2024

IJRASET

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“…Water quality monitoring: The authors in [82] proposed combining machine learning and TinyML-based devices(Raspberry Pi) to build a method to monitor and evaluate the water quality. Sensors capture data on numerous water quality variables(e.g., temperature, pH, and chemical material concentration) and communicate the data to a Raspberry Pi linked to a data center.…”

Section: B Hydrosphere-related Applicationsmentioning

confidence: 99%

A Comprehensive Survey on TinyML

Abadade,

Temouden,

Bamoumen

et al. 2023

IEEE Access

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Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.

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“…Firstly, it facilitates real-time and continuous monitoring and prediction of water quality, enhancing the responsiveness and effectiveness of water quality management. Secondly, machine learning models can automatically learn and adapt to the complex relationships within water quality data, resulting in more-accurate predictions [ 14 ]. Additionally, these models can incorporate other environmental factors and meteorological data, thereby improving the accuracy and reliability of water quality prediction.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

Wang

Qiao

et al. 2023

Entropy

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In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

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Water Quality Monitoring Using IoT & Machine Learning

Cited by 12 publications

References 0 publications

Monitoring of Water Quality using Machine Learning - A Review

Monitoring of Water Quality using Machine Learning - A Review

A Comprehensive Survey on TinyML

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

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