Development and performance comparison of optimized machine learning-based regression models for predicting energy-related carbon dioxide emissions
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-023-30955-1.pdf
Reference36 articles.
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2. Akkaya AV (2022) Coal higher heating value prediction using constituents of proximate analysis: Gaussian process regression model. Int J Coal Prep Util 42(7):1952–1967. https://doi.org/10.1080/19392699.2020.1786374
3. Akkaya AV, Cetin B (2020) Formulating a novel combined equation for coal calorific value estimation by group method data handling type neural network. Energy Sources A: Recovery Util Environ Eff. https://doi.org/10.1080/15567036.2020.1810831
4. Alade IO, Abd Rahman MA, Saleh TA (2019) Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Sol Energy 183:74–82. https://doi.org/10.1016/j.solener.2019.02.060
5. Alaganthiran JR, Anaba MI (2022) The effects of economic growth on carbon dioxide emissions in selected Sub-Saharan African (SSA) countries. Heliyon 8:e11193. https://doi.org/10.1016/j.heliyon.2022.e11193
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