The Citing articles tool gives a list of articles citing the current article. The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Meteorological AQI and pollutants concentration-based AQI predictor
S. Sachdeva, R. Kaur, Kimmi, H. Singh, K. Aggarwal and S. Kharb International Journal of Environmental Science and Technology 21(5) 4979 (2024) https://doi.org/10.1007/s13762-023-05307-8
Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China
Ahmad Hasnain, Muhammad Zaffar Hashmi, Sohaib Khan, Uzair Aslam Bhatti, Xiangqiang Min, Yin Yue, Yufeng He and Geng Wei Environmental Monitoring and Assessment 196(5) (2024) https://doi.org/10.1007/s10661-024-12644-9
Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis
Om Prakash Bera, U. Venkatesh, Gopal Krushna Pal, Siddhant Shastri, Sayantan Chakraborty, Ashoo Grover and Hari Shanker Joshi The Lancet Regional Health - Southeast Asia 30 100486 (2024) https://doi.org/10.1016/j.lansea.2024.100486
Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention
Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan
Hussein Ali Hussein, Mukhtar M. E. Mahmoud and Haroun A. Eisa EAI Endorsed Transactions on Smart Cities 7(2) e1 (2023) https://doi.org/10.4108/eetsc.v7i2.3023
Ahmad Hasnain, Muhammad Zaffar Hashmi, Zulkifl Ahmed, Uzair Aslam Bhatti, Zaeem Hassan Akhter, Xiangqiang Min, Yin Yue, Yufeng He, Basit Nadeem and Geng Wei (2023) https://doi.org/10.21203/rs.3.rs-2400753/v1
FORECASTING OF AIR POLLUTION WITH TIME SERIES AND MULTIPLE REGRESSION MODELS IN SOFIA, BULGARIA
Nikolay Stoyanov, Antonia Pandelova, Tzanko Georgiev, Julia Kalapchiiska and Bozhidar Dzhudzhev Journal of Environmental Engineering and Landscape Management 31(3) 176 (2023) https://doi.org/10.3846/jeelm.2023.19467
Trends, Projections, and Regional Disparities of Maternal Mortality in Africa (1990–2030): An ARIMA Forecasting Approach
Proceedings of International Conference on Information Technology and Applications
Ahmad Hasnain, Muhammad Zaffar Hashmi, Basit Nadeem, Mir Muhammad Nizamani and Sibghat Ullah Bazai Lecture Notes in Networks and Systems, Proceedings of International Conference on Information Technology and Applications 614 27 (2023) https://doi.org/10.1007/978-981-19-9331-2_3
Time Series Analysis and Forecasting of Air Pollutants Based on Prophet Forecasting Model in Jiangsu Province, China
Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach
Iqramul Haq, Md. Ismail Hossain, Ahmed Abdus Saleh Saleheen, et al. Interdisciplinary Perspectives on Infectious Diseases 2022 1 (2022) https://doi.org/10.1155/2022/8570089
A new approach to short-term wind speed prediction: the prophet model
Sema Atasever, Başak Öztürk and Gülbahar Bilgiç Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44(4) 8826 (2022) https://doi.org/10.1080/15567036.2022.2126035
Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET
Christophorus Beneditto Aditya Satrio, William Darmawan, Bellatasya Unrica Nadia and Novita Hanafiah Procedia Computer Science 179 524 (2021) https://doi.org/10.1016/j.procs.2021.01.036
Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh
Shihab Ahmad Shahriar, Imrul Kayes, Kamrul Hasan, Mohammed Abdus Salam and Shawan Chowdhury Air Quality, Atmosphere & Health 13(10) 1247 (2020) https://doi.org/10.1007/s11869-020-00878-8
Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea