Machine learning–based approach to predict ice meltdown in glaciers due to climate change and solutions

Pant, Piyush; Rajawat, Anand Singh; Goyal, S. B.; Chakrabarti, Prąsun; Bedi, Pradeep; Salau, Ayodeji Olalekan

doi:10.1007/s11356-023-28466-0

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Changes in the Earth's climate, apart from the increase in global air temperature, affect the snow cover [Pant et al 2023]. Its thickness, as well as the number of days with snow, is declining in many regions of Europe [Olefs et al 2020, Gentilucci et al 2023, Stucchi et al 2023, Szyga-Pluta 2021.…”

Section: Discussionmentioning

confidence: 99%

Hidden in snow: Selected aspects of chemical composition of an urban snow cover (Kielce, SE Poland)

Szwed,

Kozłowski,

Śliwa

et al. 2023

Environmental Protection and Natural Resources

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Snow cover is a valuable source of information about air quality. It enables detection of dust and other air pollutants which have been accumulated throughout the period since the snow cover was formed. Research conducted in Kielce confirms multidirectional human pressure from local and regional emission sources. Combustion of fuels resulted in lower pH and increased EC, SO4 and NO3 concentrations in the southern and northern parts of the city. Elevated concentrations of Cl and Na indicate the effect of transportation and winter road maintenance. Apart from local emitters, air quality in Kielce is affected by the regional sources, including the nearby center of the lime and cement industries. Climate change is reflected in the number of days with snow and thickness of snow cover, declining since the 1990s.

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Section: Discussionmentioning

confidence: 99%

Hidden in snow: Selected aspects of chemical composition of an urban snow cover (Kielce, SE Poland)

Szwed,

Kozłowski,

Śliwa

et al. 2023

Environmental Protection and Natural Resources

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“…We show that the hybrid algorithm can accurately predict the variability and uncertainty of green energy sources, and that it is more accurate and reliable than the other methods. Three Artificial Intelligence (AI) algorithms-Random Forest, Support Vector Machines (SVM) [11], and Deep Boltzmann Machine-are used in the plan to fight climate change by using predictive modeling to increase the use of green energy.…”

Section: Methodsmentioning

confidence: 99%

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

Alharbe,

Alluhaibi

2023

IJACSA

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This study looks at how AI algorithms like Random Forest, Support Vector Machines (SVM), and Deep Boltzmann Machine (DBM) can be used for predictive modeling to make it easier to use renewable energy sources while reducing the negative effects of climate change. Predictive models based on Artificial Intelligence show possible ways to get the most out of green energy sources, which could lead to fewer carbon emissions. The results of the preliminary studies show that these AI systems can make accurate predictions about how green energy will be made because they are good at making predictions and generalizing. This feature makes it possible to use resources effectively, which improves the reliability of the grid and encourages more people to use green energy sources. Ultimately, employing these AI programs will serve as powerful tools in combating climate change and fostering a more sustainable and eco-friendly environment.

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“…AI and machine learning are now widely applied to analyze the patterns, mechanisms, and trends in various natural phenomena with a certain level of randomness. As temperature-sensitive materials, ice/snow can benefit from AI and machine learning to assist in extracting valuable insights from available data resources, exploring the relationships between ice behavior and the main controlling factors, as well as numerous secondary controlling factors [13]. Three papers were published on the topic of snow and ice identification using machine learning or artificial intelligence.…”

Section: An Overview Of Published Articlesmentioning

confidence: 99%

Cold Region Ice/Snow Actions in Hydrology, Ecology and Engineering

Li,

Tavakoli

et al. 2024

Water

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Machine learning–based approach to predict ice meltdown in glaciers due to climate change and solutions

Cited by 5 publications

References 26 publications

Hidden in snow: Selected aspects of chemical composition of an urban snow cover (Kielce, SE Poland)

Hidden in snow: Selected aspects of chemical composition of an urban snow cover (Kielce, SE Poland)

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

Cold Region Ice/Snow Actions in Hydrology, Ecology and Engineering

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