{"title":"Prediction of meteorological parameters using statistical time series models: a case study","authors":"Naba Krushna Sabat, Rashmiranjan Nayak, Harshit Srivastava, Umesh Chandra Pati, Santos Kumar Das","doi":"10.1504/ijgw.2023.133547","DOIUrl":null,"url":null,"abstract":"Natural calamities are frequent nowadays due to global warming caused by the adverse impact created by unsustainable development and associated environmental pollution. Atmospheric weather is highly influenced by global warming. Hence, the present work predicts five important meteorological parameters responsible for weather conditions, such as temperature, humidity, pressure, wind speed, and wind direction of Bengaluru City, from the respective historical data available from January 2009 to January 2020, using statistical time series forecasting models. The comparative analysis of these statistical models shows that the vector auto-regressive moving average model outperforms other models in predicting all the above mentioned parameters.","PeriodicalId":14065,"journal":{"name":"International Journal of Global Warming","volume":"2014 1","pages":"0"},"PeriodicalIF":1.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Global Warming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijgw.2023.133547","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Natural calamities are frequent nowadays due to global warming caused by the adverse impact created by unsustainable development and associated environmental pollution. Atmospheric weather is highly influenced by global warming. Hence, the present work predicts five important meteorological parameters responsible for weather conditions, such as temperature, humidity, pressure, wind speed, and wind direction of Bengaluru City, from the respective historical data available from January 2009 to January 2020, using statistical time series forecasting models. The comparative analysis of these statistical models shows that the vector auto-regressive moving average model outperforms other models in predicting all the above mentioned parameters.
期刊介绍:
IJGW aims to bring all disciplines together for local/global solutions to combat global warming and its consequences. It focuses around nine main pillars: better remediation, avoidance, efficiency, cost effectiveness, design, resource utilisation, environmental quality, energy security, and sustainable development. It also address issues related to global changes as a direct/indirect result of climate modification and strategies for adaptation to such changes. IJGW covers disciplines as diverse as engineering, climate science, ecology, economics, education, management, information sciences, politics, strategy development, etc.