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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Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features

Journal of Environmental Management, Год журнала: 2024, Номер 358, С. 120756 - 120756

Опубликована: Апрель 9, 2024

Язык: Английский

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Predicting river water quality: An imposing engagement between machine learning and the QUAL2Kw models (case study: Aji-Chai, river, Iran)

Results in Engineering, Год журнала: 2024, Номер 21, С. 101921 - 101921

Опубликована: Фев. 22, 2024

Язык: Английский

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Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 6, 2024

Abstract The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, data-driven methods offer potential solutions for optimizing resources, integrating different aspects sustainability, informed decision-making. Sustainability research surrounds various local, regional, challenges, emphasizing need identify emerging areas gaps AI models play crucial role. study performs comprehensive literature survey scientometric semantic analyses, categorizes problems, discusses sustainable use big data. outcomes analyses highlight collaborative inclusive that bridges regional differences, interconnection topics, major themes related It further emphasizes significance developing hybrid approaches combining techniques, expert knowledge multi-level, multi-dimensional Furthermore, recognizes necessity addressing ethical concerns ensuring data in research.

Язык: Английский

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Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution

Environmental Research, Год журнала: 2024, Номер 253, С. 119142 - 119142

Опубликована: Май 14, 2024

Язык: Английский

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Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Environmental Chemistry Letters, Год журнала: 2024, Номер 22(5), С. 2293 - 2318

Опубликована: Май 21, 2024

Abstract The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in context rapid industrialization urbanization. Moreover, pollutants such as antibiotics escape conventional wastewater treatments thus discharged ecosystems, requiring advanced techniques for treatment. Here we review use artificial intelligence machine learning optimize pharmaceutical treatment systems, with focus on quality, disinfection, renewable energy, biological treatment, blockchain technology, algorithms, big data, cyber-physical automated smart grid power distribution networks. Artificial allows monitoring contaminants, facilitating data analysis, diagnosing easing autonomous decision-making, predicting process parameters. We discuss advances technical reliability, energy resources management, cyber-resilience, security functionalities, robust multidimensional performance platform distributed consortium, stabilization abnormal fluctuations quality

Язык: Английский

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Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities

Desalination and Water Treatment, Год журнала: 2024, Номер 320, С. 100618 - 100618

Опубликована: Июль 17, 2024

Язык: Английский

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A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 132, С. 107926 - 107926

Опубликована: Янв. 31, 2024

Increases in population and prosperity are linked to a worldwide rise garbage. The "classification" "recycling" of solid waste is crucial tactic for dealing with the problem. This paper presents new two-layer intelligent decision system sorting based on fused features Deep Learning (DL) models as well selection an optimal deep Waste-Sorting Model (WSM) Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples images waste, distributed across four classes – cardboard (403), glass (501), metal (410), general trash (137), was used sorting. study proposes Multi-Fused Matrix (MFDM) identified fusion score level rules, evaluation criteria, waste-sorting models. Five rules process perspectives into MFDM sum, weighted product, maximum, minimum rules. Additionally, each entropy Visekriterijumska Optimizacija i Kompromisno Resenje Serbian (VIKOR) methods weighting selected criteria ranking WSMs. highest accuracy rate 98% scored by ResNet50-GoogleNet- Inception rule. However, under same rule, insufficient presented ResNet50-GoogleNet-Xception. Since Qi = 0 Inception-Xception, final output MCDM indicates that Inception-Xception model outperforms other WSMs, which achieved lowest values Qi. Thus, chosen best multiple different perspectives. mean standard deviation metrics were both validate findings objectively. suggested approach can aid urban decision-makers prioritizing choosing Artificial Intelligence (AI)-optimized model.

Язык: Английский

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Urban transport emission prediction analysis through machine learning and deep learning techniques

Transportation Research Part D Transport and Environment, Год журнала: 2024, Номер 135, С. 104389 - 104389

Опубликована: Авг. 31, 2024

Язык: Английский

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Balancing economic growth and ecological sustainability: Factors affecting the development of renewable energy in developing countries

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 116, С. 601 - 612

Опубликована: Март 14, 2025

Язык: Английский

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