Kumar Saurabh, Mishra Shweta, Singh Sunil Kumar
Department of Computer Science & Information Technology, Mahatma Gandhi Central University, Bihar, India.
Heliyon. 2020 Nov 30;6(11):e05618. doi: 10.1016/j.heliyon.2020.e05618. eCollection 2020 Nov.
During the last many years, the air quality of the capital city of India, Delhi had been hazardous. A large number of people have been diagnosed with Asthma and other breathing-related problems. The basic reason behind this has been the high concentration of life-threatening PM2.5 particles dissolved in its atmosphere. A good model, to forecast the concentration level of these dissolved particles, may help to prepare the residents with better prevention and safety strategies in order to save them from many health-related diseases. This work aims to forecast the PM2.5 concentration levels in various regions of Delhi on an hourly basis, by applying time series analysis and regression, based on various atmospheric and surface factors such as wind speed, atmospheric temperature, pressure, etc. The data for the analysis is obtained from various weather monitoring sites, set-up in the city, by the Indian Meteorological Department (IMD). A regression model is proposed, which uses Extra-Trees regression and AdaBoost, for further boosting. Experimentation for comparative study with the recent works is done and results indicate the efficacy of the proposed model.
在过去的许多年里，印度首都德里的空气质量一直很糟糕。大量的人被诊断出患有哮喘和其他与呼吸有关的问题。其背后的根本原因是大气中溶解的危及生命的PM2.5颗粒浓度过高。一个能够预测这些溶解颗粒浓度水平的良好模型，可能有助于让居民制定更好的预防和安全策略，以使他们免受许多与健康相关疾病的困扰。这项工作旨在通过应用时间序列分析和回归方法，基于风速、大气温度、气压等各种大气和地表因素，每小时预测德里不同地区的PM2.5浓度水平。分析数据来自印度气象部门（IMD）在该市设立的多个气象监测站点。提出了一个回归模型，该模型使用极端随机树回归和自适应增强算法进行进一步增强。与近期研究进行了对比实验，结果表明了所提模型的有效性。
