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Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

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  • Amiri, Zahra
  • Heidari, Arash
  • Navimipour, Nima Jafari

Abstract

With the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.

Suggested Citation

  • Amiri, Zahra & Heidari, Arash & Navimipour, Nima Jafari, 2024. "Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s036054422402601x
    DOI: 10.1016/j.energy.2024.132827
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    1. Pankaj Verma & Nitish Katal & Bhisham Sharma & Subrata Chowdhury & Abolfazl Mehbodniya & Julian L. Webber & Ali Bostani, 2022. "Voltage Rise Mitigation in PV Rich LV Distribution Networks Using DC/DC Converter Level Active Power Curtailment Method," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    3. Zhan, Choujun & Cao, Weiwen & Fan, Junyu & Tse, C.K., 2018. "Impulse Weibull distribution for daily precipitation and climate change in China during 1961–2011," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 57-67.
    4. Joseph Nyangon & Ruth Akintunde, 2024. "Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    5. Paul X McCarthy & Xian Gong & Sina Eghbal & Daniel S Falster & Marian-Andrei Rizoiu, 2021. "Evolution of diversity and dominance of companies in online activity," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    6. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    7. Joseph Nyangon & John Byrne, 2023. "Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(1), January.
    8. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    9. Hamed Tahami & Ehsan Akbari & Adil Hussein Mohammed & Reza Faraji & Sittiporn Channumsin, 2023. "A Transformerless Enhanced-Boost Quasi-Z-Source Inverter with Low Input Current Ripple for Stand-Alone RES-Based Systems," Energies, MDPI, vol. 16(6), pages 1-33, March.
    10. Sebastian Sippel & Nicolai Meinshausen & Erich M. Fischer & Enikő Székely & Reto Knutti, 2020. "Climate change now detectable from any single day of weather at global scale," Nature Climate Change, Nature, vol. 10(1), pages 35-41, January.
    11. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    12. Michael Keane & Timothy Neal, 2020. "Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 59-80.
    13. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    14. Alireza Dehghani & Mehdi Alidadi & Ayyoob Sharifi, 2022. "Compact Development Policy and Urban Resilience: A Critical Review," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
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