Performance analysis of the water quality index model for predicting water state using machine learning techniques

Process Safety and Environmental Protection, Journal Year: 2022, Volume and Issue: 169, P. 808 - 828

Published: Nov. 28, 2022

Existing water quality index (WQI) models assess using a range of classification schemes. Consequently, different methods provide number interpretations for the same properties that contribute to considerable amount uncertainty in correct quality. The aims this study were evaluate performance model order classify coastal correctly completely new scheme. Cork Harbour data was used study, which collected by Ireland's environmental protection agency (EPA). In present four machine-learning classifier algorithms, including support vector machines (SVM), Naïve Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), utilized identify best predicting classes widely seven WQI models, whereas three are recently proposed authors. KNN (100% 0% wrong) XGBoost (99.9% 0.1% algorithms outperformed accurately models. validation results indicate outperformed, accuracy (1.0), precision (0.99), sensitivity specificity F1 (0.99) score, predict Moreover, compared higher prediction accuracy, precision, sensitivity, specificity, score found weighted quadratic mean (WQM) unweighted root square (RMS) respectively, each class. findings showed WQM RMS could be effective reliable assessing terms classification. Therefore, helpful providing accurate information researchers, policymakers, research personnel monitoring more effectively.

Language: Английский

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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

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Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128486 - 128486

Published: Dec. 14, 2022

Language: Английский

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Water Quality Prediction Using KNN Imputer and Multilayer Perceptron

Water, Journal Year: 2022, Volume and Issue: 14(17), P. 2592 - 2592

Published: Aug. 23, 2022

The rapid development to accommodate population growth has a detrimental effect on water quality, which is deteriorating. Consequently, quality prediction emerged as topic of great interest during the past decade. Existing approaches lack desired accuracy. Moreover, available datasets have missing values, reduces performance efficiency classifiers. This study presents an automatic method that resolves issue values from data and obtains higher proposes nine-layer multilayer perceptron (MLP) used with K-nearest neighbor (KNN) imputer deal problem values. Experiments are performed, compared seven machine learning algorithms. Performance further analyzed regarding two scenarios: deleting use KNN Results suggest proposed MLP model can achieve accuracy 0.99 for imputer. K-fold cross-validation corroborates this performance.

Language: Английский

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A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes

Desalination, Journal Year: 2022, Volume and Issue: 546, P. 116221 - 116221

Published: Nov. 9, 2022

Language: Английский

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Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

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A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(22), P. 12147 - 12147

Published: Nov. 8, 2023

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain water resource management. Environmental issues, such as climate change ecosystem destruction, pose significant threats to humanity planet. Addressing these challenges necessitates sustainable management increased efficiency. Artificial intelligence (AI) ML technologies present promising solutions this regard. By harnessing AI ML, we can collect analyze vast amounts data from diverse sources, remote sensing, smart sensors, social media. enables real-time monitoring decision making applications, including irrigation optimization, quality monitoring, flood forecasting, demand enhance agricultural practices, distribution models, desalination plants. Furthermore, facilitates integration, supports decision-making processes, enhances overall sustainability. However, wider adoption faces challenges, heterogeneity, stakeholder education, high costs. To provide an management, research focuses on core fundamentals, major (prediction, clustering, reinforcement learning), ongoing issues offer new insights. More specifically, after in-depth illustration algorithmic taxonomy, comparative mapping all specific tasks. At same time, include tabulation works along with some concrete, yet compact, descriptions objectives at hand. leveraging tools, develop plans address world’s supply concerns effectively.

Language: Английский

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Recent Advances in Artificial Intelligence Sensors

Advanced Sensor Research, Journal Year: 2023, Volume and Issue: 2(8)

Published: April 7, 2023

Abstract Significant growth in the development and deployment of artificial intelligence (AI) is being witnessed. Driven by great versatility emerging computer science material science, various AI sensors provide cost‐effective approaches for a wide range monitoring applications toward realization smart homes personal healthcare. Advanced have multiple capable detecting multidimensional information human‐brain‐like computation device data processing. Herein, this review outlines recent advances sensors. This first introduces materials, fabrication methods, algorithms current their applications, i.e., complementary metal oxide semiconductor image vision, microelectromechanical systems, microphone voice recognition, wearable gesture recognition. Then, wearables self‐powered sensor systems are highlighted. Next, developments neuromorphic computing multimodality, digital twins reviewed. Last, perspective on future directions further research also provided. In summary, trend advanced between edge cloud computing, which will show potential buildings, individual healthcare, Internet things, etc.

Language: Английский

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A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of Modified Niched Genetic Algorithm

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 240, P. 122307 - 122307

Published: Oct. 28, 2023

Language: Английский

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Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141393 - 141393

Published: Feb. 5, 2024

Language: Английский

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