Modeling Land Surface Temperature on Local Scale using Machine Learning Algorithm and Analyze of Spectral Indices and Air Quality Parameters during the Lockdown period, before and after that.

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نوع مقاله : مقاله پژوهشی

نویسنده
امیرارسلان غفوری
دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.
10.22034/he.2026.589654.1242
چکیده
The SARS CoV-2 (COVID-19) outbreak poses a serious threat to humans, society, and economic activity around the world. Strict global control measures to limit virus spread have a number of positive environmental consequences. The study provides an unprecedented opportunity to understand and quantify the human impact on the atmospheric environment. New York City has seen a decrease in the average LST and a negative growth in the rate of change of this key parameter to the amount of (-0.83%) in NYC. The LST was validated with air temperature taken from air temperature measured at meteorological stations measured over NYC (RMSE = 6.34 °C and R2 = 0.94). On the other hand, the quarantine plans reduced the concentrations of NO2, SO2, O3, and CO, and the rate of change of these parameters during the quarantine period was negative and included (NO2 (-30.0%), O3 (-31.25%), (0) SO2, and CO (-25%)). During the lockdown, reducing the number of moving vehicles and people on the roads reduces pollution and has a direct positive impact on the urban environment. This study compares the environmental status of the index (NDVI), NDBI, NDWI, and Albedo during the lockdown period in NYC to pre-lockdown (2015-2019) and post-lockdown (2021). The results show that the NDVI and NDWI have a positive change rate during the lock period, respectively; the rate of change of these two during the lock period includes (0.45 %, 2.29%), and the range of changes during this period was negative in the case of the NDBI index (-0.85%). Finally, the main goal of this study is to model LST at the local scale using an Artificial Neural Network (ANN). Therefore, land surface spectral indices known as land cover indices as well as air quality parameters can be used as indices that affect LST. The relationship between six spectral indices and air quality parameters with LST was investigated, and the indices that had a high correlation with LST were used as input to the neural network. Due to the fact that the results of correlation with LST were different in each period, the inputs of the neural network were different in each period. In the pre-lockdown, the input parameters include (NDBI, Albedo, CO, and NDWI), in the period of lockdown (NDWI, NDBI, SO2, and NO2), and post-lockdown (NDBI, Albedo, CO, and NDWI). The ANN model provided accurate results in the training and testing process with (RMSE) and (R2) values, respectively, in all three periods (RMSE:0.004, R2: 071), (RMSE:0.003, R2: 073), (RMSE:0.003, R2: 072).
کلیدواژه‌ها
Artificial Neural Network
Air Quality
COVID-19
Land Surface Temperature
موضوعات

عنوان مقاله English

Modeling Land Surface Temperature on Local Scale using Machine Learning Algorithm and Analyze of Spectral Indices and Air Quality Parameters during the Lockdown period, before and after that.

نویسنده English

Amir Arsalan Ghafouri
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
چکیده English

The SARS CoV-2 (COVID-19) outbreak poses a serious threat to humans, society, and economic activity around the world. Strict global control measures to limit virus spread have a number of positive environmental consequences. The study provides an unprecedented opportunity to understand and quantify the human impact on the atmospheric environment. New York City has seen a decrease in the average LST and a negative growth in the rate of change of this key parameter to the amount of (-0.83%) in NYC. The LST was validated with air temperature taken from air temperature measured at meteorological stations measured over NYC (RMSE = 6.34 °C and R2 = 0.94). On the other hand, the quarantine plans reduced the concentrations of NO2, SO2, O3, and CO, and the rate of change of these parameters during the quarantine period was negative and included (NO2 (-30.0%), O3 (-31.25%), (0) SO2, and CO (-25%)). During the lockdown, reducing the number of moving vehicles and people on the roads reduces pollution and has a direct positive impact on the urban environment. This study compares the environmental status of the index (NDVI), NDBI, NDWI, and Albedo during the lockdown period in NYC to pre-lockdown (2015-2019) and post-lockdown (2021). The results show that the NDVI and NDWI have a positive change rate during the lock period, respectively; the rate of change of these two during the lock period includes (0.45 %, 2.29%), and the range of changes during this period was negative in the case of the NDBI index (-0.85%). Finally, the main goal of this study is to model LST at the local scale using an Artificial Neural Network (ANN). Therefore, land surface spectral indices known as land cover indices as well as air quality parameters can be used as indices that affect LST. The relationship between six spectral indices and air quality parameters with LST was investigated, and the indices that had a high correlation with LST were used as input to the neural network. Due to the fact that the results of correlation with LST were different in each period, the inputs of the neural network were different in each period. In the pre-lockdown, the input parameters include (NDBI, Albedo, CO, and NDWI), in the period of lockdown (NDWI, NDBI, SO2, and NO2), and post-lockdown (NDBI, Albedo, CO, and NDWI). The ANN model provided accurate results in the training and testing process with (RMSE) and (R2) values, respectively, in all three periods (RMSE:0.004, R2: 071), (RMSE:0.003, R2: 073), (RMSE:0.003, R2: 072).

کلیدواژه‌ها English

Artificial Neural Network
Air Quality
COVID-19
Land Surface Temperature
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