Towards harmonized standards for freshwater biodiversity monitoring and biological assessment using benthic macroinvertebrates

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 918, P. 170360 - 170360

Published: Feb. 2, 2024

Monitoring programs at sub-national and national scales lack coordination, harmonization, systematic review analysis continental global scales, thus fail to adequately assess evaluate drivers of biodiversity ecosystem degradation loss large spatial scales. Here we the state art, gaps challenges in freshwater assessment for both biological condition (bioassessment) monitoring ecosystems using benthic macroinvertebrate community. To existence nationally- regionally- (sub-nationally-) accepted protocols that are put practice/used each country, conducted a survey from November 2022 May 2023. Responses 110 respondents based 67 countries were received. Although responses varied their consistency, clearly demonstrated being done levels lakes, rivers artificial waterbodies. Programs bioassessment more widespread, some cases even harmonized among several countries. We identified 20 challenges, which classed into five major categories, these (a) field sampling, (b) sample processing identification, (c) metrics indices, (d) assessment, (e) other challenges. Above all, identify harmonization as one most important gaps, hindering efficient collaboration communication. IUCN SSC Global Freshwater Macroinvertebrate Sampling Protocols Task Force (GLOSAM) means address globally-harmonized protocols.

Language: Английский

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Artificial intelligence in groundwater management: Innovations, challenges, and future prospects

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 502 - 512

Published: Jan. 26, 2024

The integration of Artificial Intelligence (AI) in groundwater management is a transformative stage, characterized by innovation and challenges. This research paper explores the multilayered application AI this field, dividing its contributions, addressing associated challenges, revealing prospects future potential. AI-driven innovations are designed to revolutionize management, providing precise predictive modeling, real-time monitoring, data integration. However, these face challenges such as interpretability issues, specialized technical expertise requirements, limited quality quantity for effective model performance. In future, holds significant promise management. Advanced models can yield improved predictions behavior, identify vulnerable areas prone pollution depletion, prompt proactive interventions, foster collaborative platforms among scientists, policymakers, local communities. Collaborative driven offer potential synergistic engagement communities, collectively guiding resource Embracing AI's while remains pivotal sustainable resilient practices. By embracing landscape will continue evolve.

Language: Английский

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Tools, techniques, and trends in sustainable software engineering: A critical review of current practices and future directions

World Journal of Advanced Engineering Technology and Sciences, Journal Year: 2024, Volume and Issue: 11(1), P. 231 - 239

Published: Feb. 17, 2024

The quest for sustainability has extended its reach into the realm of software engineering, prompting an exploration tools, techniques, and emerging trends to mitigate environmental impact development operation. This review provides a critical current practices future directions in sustainable engineering. In recent years, industry recognized need address footprint systems, considering factors such as energy consumption, resource utilization, carbon emissions. Consequently, plethora tools techniques have emerged support processes. These range from energy-efficient programming languages frameworks eco-friendly architectures design patterns. Moreover, methodologies Green Software Engineering (GSE) Sustainable Development (SSD) gained traction, emphasizing integration considerations throughout lifecycle. By adopting like green requirements energy-aware testing, eco-design principles, organizations can optimize their systems reduced without compromising functionality or performance. Furthermore, engineering extend beyond traditional practices. rise cloud computing, edge Internet Things (IoT) technologies presents both challenges opportunities sustainability. Techniques serverless computing containerization offer potential benefits terms efficiency scalability, while also introducing new regarding data center consumption electronic waste management. Looking ahead, is marked by innovation collaboration. Emerging artificial intelligence (AI) blockchain hold promise optimizing allocation, enhancing efficiency, fostering transparency efforts. Additionally, interdisciplinary collaboration between engineers, scientists, policymakers will be essential shaping more digital ecosystem. journey towards involves multifaceted approach encompassing ongoing adaptation evolving trends. critically evaluating embracing directions, contribute environmentally responsible resilient future.

Language: Английский

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Cascading Nature Risks: Applying the Rumsfeld Matrix to Case Studies on Pollinator Decline, an AMOC Collapse, and Zoonotic Pandemics

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and Conservation: Transformative Potential and Future Perspectives

Plants, Journal Year: 2025, Volume and Issue: 14(7), P. 998 - 998

Published: March 22, 2025

Plants serve as the basis for ecosystems and provide a wide range of essential ecological, environmental, economic benefits. However, forest plants other systems are constantly threatened by degradation extinction, mainly due to misuse exhaustion. Therefore, sustainable management (SFM) is paramount, especially in wake global climate change challenges. SFM ensures continued provision forests both present future generations. In practice, faces challenges balancing use conservation forests. This review discusses transformative potential artificial intelligence (AI), machine learning, deep learning (DL) technologies management. It summarizes current research technological improvements implemented using AI, discussing their applications, such predictive analytics modeling techniques that enable accurate forecasting dynamics carbon sequestration, species distribution, ecosystem conditions. Additionally, it explores how AI-powered decision support facilitate adaptive strategies integrating real-time data form images or videos. The manuscript also highlights limitations incurred ML, DL combating management, providing acceptable solutions these problems. concludes perspectives immense modernizing SFM. Nonetheless, great deal has already shed much light on this topic, bridges knowledge gap.

Language: Английский

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Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa

Systems and Soft Computing, Journal Year: 2023, Volume and Issue: 5, P. 200049 - 200049

Published: Feb. 10, 2023

The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement resources, over-exploitation, inadequate supply surface water pollution have led to severe drought an drop resources' levels over past decades. To address this, a flow model several mathematical data-driven models been developed for forecasting levels. However, there is problem unavailability scarcity on-site input data needed by forecast level. Furthermore, as result dynamics stochastic characteristics groundwater, need appropriate, accurate reliable solve these challenges. Over years, broad application Machine Learning (ML) Artificial Intelligence (AI) are gaining attraction alternative solution Against this background, article provides overview methods predicting Also, uses ML such Regressions Models, Deep Auto-Regressive models, Nonlinear Autoregressive Neural Networks with External Input (NARX) using region 10 at Karst belt South Africa case study. This was done Python Mx. Version 1.9.1., MATLAB R2022a machine learning toolboxes. Moreover, Coefficient Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Absolute Percentage (MAPE), Squared (MSE), (MAE), Scaled (MASE)) were statistical performance metrics used assess predictive models. results obtained showed that NARX Support Vector (SVM) higher accuracy compared other

Language: Английский

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Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues

Green and Low-Carbon Economy, Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 21, 2023

Artificial Intelligence (AI) is used to create more sustainable production methods and model climate change, making it a valuable tool in the fight against environmental degradation. This paper describes paradox of an energy-consuming technology serving ecological challenges tomorrow. The study provides overview sectors that use AI-based solutions for protection. It draws on numerous examples from AI Green players present cases concrete examples. In second part study, negative impacts environment emerging technological support are examined. also shown research less motivated by cost energy autonomy constraints than considerations. leads rebound effect favors increase complexity models. Finally, need integrate indicators into algorithms discussed. dimension broader ethical problem AI, addressing crucial ensuring sustainability long term.

Language: Английский

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Artificial Intelligence in Fisheries and Aquaculture: Enhancing Sustainability and Productivity

Archives of Current Research International, Journal Year: 2024, Volume and Issue: 24(3), P. 106 - 123

Published: March 2, 2024

According to its definition, artificial intelligence (AI) is "the future built from fragments of the past." These are applications that acquire novel solutions with practice. Artificial has been used in various disciplines, agriculture full industry automation. Thanks AI, aquaculture become a less labor-intensive industry, enabling fisheries sector grow quickly and triple production quickly. It can appear as any laborer at work, such feeders, water quality monitors, harvesters, processors, etc. AI even be employed protect aquatic life types extinction. monitors fishing activity worldwide promotes open sea fisheries' sustainability. plays significant role combating IUU fishing. limit input waste cut costs by up 30%. As result, offers total control over fish systems lower maintenance cost. AI's integration into transformed enabled sustainable growth, increased productivity cost savings while minimizing environmental impact labor requirements. Through application technologies, meet growing demand for seafood addressing challenges overfishing, degradation, resource scarcity.

Language: Английский

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Machine intelligence applied to sustainability: A systematic methodological proposal to identify sustainable animals

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 420, P. 138292 - 138292

Published: July 31, 2023

Language: Английский

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Intelligent Conservation

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 215 - 226

Published: March 29, 2024

Artificial Intelligence (AI) emerges as a potent ally in augmenting environmental monitoring and fortifying conservation efforts. Now we have seen escalating challenges the need for sustainable practices. This paper outlines innovative applications transformative potential of AI managing complexities ecological preservation monitoring. facilitates real-time processing interpretation voluminous data. It helps informed decision-making strategic planning initiatives. The employment AI-driven models technologies such machine learning algorithms, computer vision sensor networks has proven instrumental biodiversity. plays pivotal role enabling precision by facilitating identification prioritization critical areas requiring immediate intervention. contributes to development smart adaptive systems capable autonomously tracking analysing disturbances human encroachments.

Language: Английский

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