Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics
Climate change; Linear regression; Machine learning; Random forest; Sustainability
Sustainable features are dependent on vital climatic elements that has a prominent impact on the retention of sustainability provided its metrics are in desired domain. Regression analysis and ensemble learning models are some of the predictive analytics methods which were used to detect the association of every feature on sustainable criteria. Weather samples from Delhi during 1970-2020 is used in the research which considers features like humidity, pollutant level, temperature etc which are gathered from several authenticated sites like pollution management unit of India. After analyzing several elements affecting weather endurability, it is noticed that pollutant level and temperature exhibit the highest significance recording 30% and 44% respectively. Also the R-square metric of 86% and 82% was observed with implementation of analytics models. The major conclusion recorded that random forest outperformed regression model and it established the importance of predictive analytics in predicting sustainability results. The research validated the relevance of climatic tracking for regulating sustainability. 2023 IEEE.
Pattanaik A.; Sharma V.; Kunj K.; Mishra S.; Iwendi C.; Osamor J.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICCAKM58659.2023.10449578 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187553292&doi=10.1109%2fICCAKM58659.2023.10449578&partnerID=40&md5=26a71c24c5e842aa6df092d1ec7e5f76
Pattanaik A., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Kunj K., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Mishra S., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 0BA, United Kingdom