Open Access
Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
---|---|---|
Article Number | 01025 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/epjconf/202532801025 | |
Published online | 18 June 2025 |
- S.-J. Park, D.-K. Lee, Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms. Environ. Res. Lett. 15, 094052 (2020). https://doi.org/10.1088/1748-9326/aba5b3. [CrossRef] [Google Scholar]
- M. Meghana, S. Radhika, V.S. Kumari, Anomaly Detection for Vertical Plant Wall System using Novel Support Vector Machine in comparison with Linear Regression for improving accuracy, in Proceedings of ICONSTEM-2023 conference, IEEE, Chennai, India, April 06-07 (2023). https://doi.org/10.1109/iconstem56934.2023.10142459. [Google Scholar]
- T. Khan, J. Qiu, A. Banjar, R. Alharbey, A.O. Alzahrani, R. Mehmood, Effect of climate change on fruit by co-integration and machine learning. Int. J. Clim. Change Strat. Manag. 13, 208–226 (2021).https://doi.org/10.1108/iiccsm-09-2020-0097. [CrossRef] [Google Scholar]
- M.C. Fitzpatrick, V.E. Chhatre, R.Y. Soolanayakanahally, S.R. Keller, Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).https://doi.org/10.1111/1755-0998.13374. [CrossRef] [PubMed] [Google Scholar]
- A. Zia, K. Lacasse, N.H. Fefferman, L.J. Gross, B. Beckage, Machine Learning a Probabilistic Structural Equation Model to Explain the Impact of Climate Risk Perceptions on Policy Support. Sustainability 16, 10292, (2024). https://doi.org/10.3390/su162310292. [CrossRef] [Google Scholar]
- C. Choudhary, N. Vyas, U.K. Lilhore, An Optimized Sign Language Recognition Using Convolutional Neural Networks (CNNs) and Tensor-Flow, in Proceedings of the ICTACS-2023 conference, IEEE, Tashkent, Uzbekistan, November 01 (2023). https://doi.org/10.1109/ictacs59847.2023.10390276. [Google Scholar]
- C. Radin, V. Nieves, M. Vicens-Miquel, J.L. Alvarez-Morales, Harnessing Machine Learning to Decode the Mediterranean's Climate Canvas and Forecast Sea Level Changes. Climate 12, 127 (2024). https://doi.org/10.3390/cli12080127. [CrossRef] [Google Scholar]
- P. Pant, A.S. Rajawat, S.B. Goyal, P. Chakrabarti, P. Bedi, A.O. Salau, Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions. Environ. Sci. Pollut. Res. 30, 125176–125187 (2023). https://doi.org/10.1007/s11356-023-28466-0. [CrossRef] [Google Scholar]
- Y.S. Hegazi, Resilience Adaptation Approach for Reducing the Negative Impact of Climate Change on Coastal Heritage Sites through Machine Learning. Appl. Sci. 12, 10916 (2022). https://doi.org/10.3390/app122110916. [CrossRef] [Google Scholar]
- S. Jain, N. Sharma, M. Kumar, FraudFort: Harnessing Machine Learning for Credit Card Fraud Detection, in Proceedings of the TIACOMP-2024 conference, IEEE, Bali, Indonesia, June 29 (2024). https://doi.org/10.1109/tiacomp64125.2024.00017. [Google Scholar]
- L. Zhuang, C. Ke, Y. Cai, V. Nourani, Measuring glacier changes in the Tianshan Mountains over the past 20 years using Google Earth Engine and machine learning. J. Geogr. Sci. 33, 1939–1964 (2023). https://doi.org/10.1007/s11442-023-2160-4. [CrossRef] [Google Scholar]
- G. Sireesha Naidu, M. Pratik, S. Rehana, Modelling hydrological responses under climate change using machine learning algorithms-semi-arid river basin of peninsular India. H2Open J. 3, 481–498 (2020). https://doi.org/10.2166/h2oj.2020.034. [CrossRef] [Google Scholar]
- G. Sharma, B. Bade, IoT and Machine Learning for Identifying Correlation between Factors Causing Climate Change. J. Electron. Inf. Syst. 2, 10–12 (2020). https://doi.org/10.30564/jeisr.v2i1.2020. [CrossRef] [Google Scholar]
- A. Rahman, R. Saha, D. Goswami, A.A. Mintoo, Climate Data Management Systems: Systematic Review Of Analytical Tools For Informing Policy Decisions. Innovatech Eng. J. 1, 1–21 (2024). https://doi.org/10.70937/faet.v1i0L3. [CrossRef] [Google Scholar]
- J. Abdullahi, A. Tahsin, M.I. Yesilnacar, A.I. Karabulut, O. Daramola, Assessment of Climate Change Impact on Precipitation Using Machine Learning Based Statistical Downscaling Method. E3S Web of Conf. 489, 04004 (2024). https://doi.org/10.1051/e3sconf/202448904004. [CrossRef] [EDP Sciences] [Google Scholar]
- M. Saldana-Perez, G. Guzman, C. Palma-Preciado, A. Argüelles-Cruz, M. Moreno-Ibarra, Geospatial modeling of climate change indices at Mexico City using machine learning regression. Transform. Gov. People Process Policy 18, 353–367 (2024). https://doi.org/10.1108/tg-10-2023-0153. [Google Scholar]
- P. Bhardwaj, C. Choudhury, P. Batra, Automating Data Analysis with Python: A Comparative Study of Popular Libraries and their Application, in Proceedings of the ICTACS-2023 conference, IEEE, Tashkent, Uzbekistan, November 01 (2023).https://doi.org/10.1109/ictacs59847.2023.10390032. [Google Scholar]
- T. Vishvakarma, Climate Change Forecasting using Machine Learning Algorithms. Int. J. Res. App. Sci. Eng. Technol. 10, 881–888 (2022). https://doi.org/10.22214/ijraset.2022.47098. [CrossRef] [Google Scholar]
- R. Tulsyan, P. Shukla, N. Arora, T. Singh, M. Kumar, AI Prediction of Stock Market Trends: An Overview for Non-Technical Researchers. Adv. Econ. Bus. Manag. Res. 296, 341–353 (2024). https://doi.org/10.2991/978-94-6463-544-7 22. [CrossRef] [Google Scholar]
- E. Isaev, B. Ajikeev, U. Shamyrkanov, K. Kalnur, K. Maisalbek, R.C. Sidle, Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek. Aerosol Air Qual. Res. 22, 210336 (2022). https://doi.org/10.4209/aaqr.210336. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.