Analysis of Global Warming Using Machine Learning

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Computational Water, Energy, and Environmental Engineering

ISSN Print: 2168-1562
ISSN Online: 2168-1570
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"Analysis of Global Warming Using Machine Learning"
written by Harvey Zheng,
published by Computational Water, Energy, and Environmental Engineering, Vol.7 No.3, 2018

has been cited by the following article(s):

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[11]	An Analysis of the Relationship Between Temperature Rise and GDP Using Machine Learning Techniques
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[12]	Prediction of Carbon Dioxide Level Using Statistical Learning and Its Potential Correlation With Global Warming
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[14]	Neural network to quantify the amount of greenhouse gases produced by flue gases affecting climatic conditions in Iran's southern areas
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[15]	Sustainability analysis of carbon dioxide emissions on combustion of biomass for electricity
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[16]	Global Warming and Bangladesh: A Machine Learning Approach to Analyze the Warming Rate Utilizing Neural Network
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[17]	Achieving sustainable development goals coupling earth observation data with machine learning
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[18]	Predicting dairy cattle heat stress using machine learning techniques
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[19]	Identifying and Mitigating Heat Stress of Grazing Dairy Cattle Using Shade and Sprinklers
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[20]	Using Machine Learning Algorithms to Evaluate the Relationship Between Air Quality and Temperature Change
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[21]	Machine Learning approach for predicting temperature in Bangladesh
	2018

[1]	Accurate prediction of air quality response to emissions for effective control policy design Journal of Environmental Sciences, 2023 DOI:10.1016/j.jes.2022.02.009

[2]	Neural network to quantify the amount of greenhouse gases produced by flue gases affecting climatic conditions in Iran's southern areas Geoenergy Science and Engineering, 2023 DOI:10.1016/j.petrol.2022.111224

[3]	The Fourth Industrial Revolution and Beyond Lecture Notes in Electrical Engineering, 2023 DOI:10.1007/978-981-19-8032-9_2

[4]	Climatic Temperature Forecasting with Regression Approach 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 2023 DOI:10.1109/ICIEM59379.2023.10166883

[5]	An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model Mathematics, 2023 DOI:10.3390/math11143060

[6]	Neural network to quantify the amount of greenhouse gases produced by flue gases affecting climatic conditions in Iran's southern areas Geoenergy Science and Engineering, 2023 DOI:10.1016/j.petrol.2022.111224

[7]	Prediction of Carbon Dioxide Level Using Statistical Learning and Its Potential Correlation With Global Warming SHS Web of Conferences, 2023 DOI:10.1051/shsconf/202317403034

[8]	Predicting the future tendencies in global warming using orthogonal projection of vectors 2ND INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMPUTATIONAL TECHNIQUES, 2023 DOI:10.1063/5.0148416

[9]	Intelligent Computing and Optimization Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-3-031-36246-0_30

[10]	Climatic Temperature Forecasting with Regression Approach 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 2023 DOI:10.1109/ICIEM59379.2023.10166883

[11]	Accurate prediction of air quality response to emissions for effective control policy design Journal of Environmental Sciences, 2022 DOI:10.1016/j.jes.2022.02.009

[12]	Predicting dairy cattle heat stress using machine learning techniques Journal of Dairy Science, 2021 DOI:10.3168/jds.2020-18653

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