日本地球惑星科学連合2018年大会

講演情報

[EE] Eveningポスター発表

セッション記号 A (大気水圏科学) » A-CG 大気水圏科学複合領域・一般

[A-CG36] 衛星による地球環境観測

2018年5月24日(木) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:沖 理子(宇宙航空研究開発機構)、本多 嘉明(千葉大学環境リモートセンシング研究センター)、高薮 縁(東京大学 大気海洋研究所、共同)、松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)

[ACG36-P06] An Artificial Intelligence Approach for Situation-Aware Prediction of Air Quality, based on MODIS and MISR Big Data Time Series

*Singh Samiksha1Singh Ananjay2 (1.Banaras Hindu University, Varanasi、2.Indian Institute of Technology Roorkee)

キーワード:Artificial Intelligence、Air Quality Index (AQI)、Situation-Aware、Big Data、Time Series

The continuously deteriorating air quality in major developing nations is a challenge for sustainability of healthy human race. One of the worst recent toxic smog formations was in the National Capital Territory of Delhi (NCT) in India during November-December 2017. Finding the multidimensional causality aspects of this dangerously hazardous situation is an important direction of research before finding any practical recommendations; henceforth any resilient solution. This research presents a porotype model for utilization of heterogeneous Satellite and ground-based sensor data sets comprising Air Quality Index (AQI), Satellite retrievals of aerosol optical depth (AOD) and surface reflectance in various bands, to identify the causality patterns(particularly the effect of kharif crop shoot burning in neighboring states of Delhi (NCT) viz. Punjab, Haryana and Uttar Pradesh). The approach comprises Artificial Intelligence (AI) techniques for logical representation of heterogeneous satellite Big Data and data from in-situ ground-based sensor, learning causality relation weights supplemented with inference and prediction capabilities. We have utilized Satellite retrievals of aerosol optical depth (AOD) from MODIS and MISR (Multi-angle Imaging Spectroradiometer) Satellites along with New Delhi Historical data of Air Quality Index (AQI) from AirNow(Environment Protection Agency, USA) and data from Central Pollution Control Board, Government of India. This prototype approach of AI derived process pipeline can be easily replicated for other locations with capabilities such as timely forecast/warning issue and other long-term recommendations.