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

Applied Sciences, Год журнала: 2023, Номер 13(22), С. 12147 - 12147

Опубликована: Ноя. 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.

Язык: Английский

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Environments, Год журнала: 2023, Номер 10(10), С. 170 - 170

Опубликована: Окт. 2, 2023

This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest use satellites as innovative approach for monitoring quality, a critical step towards addressing challenges posed by rising anthropogenic pollution. Traditional methods have limitations, but satellite sensors provide potential solution that lowering costs expanding temporal spatial coverage. However, conventional statistical are limited when faced with formidable challenge conducting recognition analysis geospatial big data because they characterized high volume complexity. As compelling alternative, deep techniques has emerged indispensable tool, remarkable capability discern intricate patterns might otherwise remain elusive traditional statistics. study employed targeted search strategy, utilizing specific criteria titles 332 journal articles indexed Scopus, resulting inclusion 165 meta-analysis. Our comprehensive provides insights into trends, productivity, impact It highlights key journals publishers this domain while examining relationship between first author’s presentation, publication year, citation count, factor. major findings highlight widespread including MultiSpectral Instrument (MSI), Ocean Land Color (OLCI), Operational Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Plus (ETM+), practice multi-sensor fusion. Deep neural networks identified popular high-performing algorithms, significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer field learning. Chlorophyll-a clarity indicators receive special attention, geo-location had optical classes. contributes significantly providing extensive examples in-depth discussions code, well highlighting cyber infrastructure used research. Advances high-performance computing, large-scale processing capabilities, availability open-source software facilitating growing prominence applications artificial intelligence monitoring, positively contributing

Язык: Английский

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Explainable Artificial Intelligence for Reliable Water Demand Forecasting to Increase Trust in Predictions

Water Research, Год журнала: 2024, Номер 268, С. 122779 - 122779

Опубликована: Ноя. 9, 2024

Язык: Английский

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Predicting Water Saturation in a Greek Oilfield with the Power of Artificial Neural Networks

ACS Omega, Год журнала: 2025, Номер 10(1), С. 557 - 566

Опубликована: Янв. 3, 2025

Water saturation plays a vital role in calculating the volume of hydrocarbon reservoirs and defining net pay. It is also essential for designing well completion. Innacurate water calculation can lead to poor decision-making, significantly affecting reservoir's development production, potentially resulting reduced oil recovery. Various techniques estimate both clean shaly formations. However, most widely used approaches petroleum industry rely on petrophysical models, including Archie's equation, Waxman-Smits, Simandoux, Indonesia, dual-water models. Most these methods are only valid sands or carbonate, while presence clay limits accuracy On other hand, estimation through core analysis does not usually cover large interval well, highly costly, requires much time. In this study, an empirical equation predicting based weight biases artificial neural networks (ANN) was developed. 334 data points shale volume, formation deep resistivity, porosity, permeability their corresponding collected from Epsilon Field Greece were considered optimizing ANN model. The model trained 252 sets, where predicted with average absolute percentage error (AAPE) 0.90%. Then, developed optimized its weights biases. remaining 82 sets (testing data) AAPE 1.08%. newly established correlation enhances precision prediction provides cost-effective means acquire continuous profile, critical asset oilfield management exploration.

Язык: Английский

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Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources

Environmental Data Science, Год журнала: 2025, Номер 4

Опубликована: Янв. 1, 2025

Abstract Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority the world’s freshwater have inadequate monitoring critical needed management. Yet, need to widespread predictions hydrological such as river flow and quality has become increasingly urgent due climate land use change over past decades, their associated impacts on resources. Modern machine learning methods outperform process-based empirical model counterparts hydrologic time series prediction with ability extract information from large, diverse data sets. We review relevant state-of-the art applications streamflow, quality, other discuss opportunities improve emerging incorporating watershed characteristics process knowledge into classical, deep learning, transfer methodologies. analysis here suggests most prior efforts been focused frameworks built many at daily scales United States, but that comparisons between different classes are few inadequate. identify several open questions include inputs site characteristics, mechanistic understanding spatial context, explainable AI techniques modern frameworks.

Язык: Английский

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Enhancing water quality management through artificial intelligence and machine learning technologies

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 69 - 88

Опубликована: Янв. 1, 2025

Язык: Английский

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Integration of Machine Learning Augmented With Biosensors for Enhanced Water Quality Monitoring

Advances in environmental engineering and green technologies book series, Год журнала: 2024, Номер unknown, С. 181 - 192

Опубликована: Янв. 22, 2024

Monitoring water quality is essential to guaranteeing the sustainability and safety of supplies. Conventional techniques for evaluating might be laborious may not able provide results instantly. The suggested system makes use a wide range biosensors assess important aspects quality, including microbial activity, pH, dissolved oxygen, chemical pollutants. Following collection, data are analysed using recurrent neural networks (RNNs). An RNN trained identify patterns, correlate information from several sensors, forecast trends in quality. Early detection problems with prompt reaction possible contaminants, flexibility response changing environmental conditions some benefits this integrated approach. enhanced monitoring (BEWQM) useful tool long-term management because its learning characteristics, which allow it continuously improve accuracy performance over time.

Язык: Английский

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Enhanced Mastitis Severity Classification in Dairy Cows Using DNN and RF: A Study on PCA and Correlation-Based Feature Selection

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100667 - 100667

Опубликована: Дек. 1, 2024

Язык: Английский

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Comprehensive comparison of modified deep convolutional neural networks for automated detection of external and middle ear conditions

Neural Computing and Applications, Год журнала: 2024, Номер 36(10), С. 5529 - 5544

Опубликована: Янв. 10, 2024

Abstract Otitis media disease, a frequent childhood ailment, could have severe repercussions, including mortality. This disease induces permanent hearing loss, commonly seen in developing countries with limited medical resources. It is estimated that approximately 21,000 people worldwide die from reasons related to this each year. The main aim of study develop model capable detecting external and middle ear conditions. Experiments were conducted find the most successful among modified deep convolutional neural networks within two scenarios. According results, EfficientNetB7 detect normal, chronic otitis media, earwax, myringosclerosis cases high accuracy Scenario 2. offers average values 99.94% accuracy, 99.86% sensitivity, 99.95% specificity, precision. An expert system based on expected provide second opinion doctors conditions, particularly primary healthcare institutions hospitals lacking field specialists.

Язык: Английский

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Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers, Southwestern Tunisia

Acta Geochimica, Год журнала: 2024, Номер 43(6), С. 1065 - 1086

Опубликована: Апрель 30, 2024

Язык: Английский

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