Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Jagadish Kumar Mogaraju

Makale detayları

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Yazarlar:

Jagadish Kumar Mogaraju

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Land Management | Geospatial Information Systems and Geospatial Data Modelling | Geographical Information Systems (GIS) in Planning

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 2, pp.233-246

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323613

pISSN:

2548-0960

Anahtar kelimeler:

Özet:

 Remote sensing RS, Geographic information systems GIS, and Machine learning can be integrated to predict land surface temperatures LST based on the data related to carbon monoxide CO, Formaldehyde HCHO, Nitrogen dioxide NO2, Sulphur dioxide SO2, absorbing aerosol index AAI, and Aerosol optical depth AOD. In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier ET, Logistic regressors LR, and Random Forests RF. The accuracy of the LR classifier 0.89 or 89 is higher than ET 82 and RF 82 classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve AUC, Recall, Precision, F1 score, Kappa, and MCC Matthews correlation coefficient. Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales 2019 to 2022. In order to evaluate the model graphically, ROC Receiver operating characteristic curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE t-distributed stochastic neighbour embedding plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

 Remote sensing RS, Geographic information systems GIS, and Machine learning can be integrated to predict land surface temperatures LST based on the data related to carbon monoxide CO, Formaldehyde HCHO, Nitrogen dioxide NO2, Sulphur dioxide SO2, absorbing aerosol index AAI, and Aerosol optical depth AOD. In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier ET, Logistic regressors LR, and Random Forests RF. The accuracy of the LR classifier 0.89 or 89 is higher than ET 82 and RF 82 classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve AUC, Recall, Precision, F1 score, Kappa, and MCC Matthews correlation coefficient. Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales 2019 to 2022. In order to evaluate the model graphically, ROC Receiver operating characteristic curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE t-distributed stochastic neighbour embedding plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

 Remote sensing RS, Geographic information systems GIS, and Machine learning can be integrated to predict land surface temperatures LST based on the data related to carbon monoxide CO, Formaldehyde HCHO, Nitrogen dioxide NO2, Sulphur dioxide SO2, absorbing aerosol index AAI, and Aerosol optical depth AOD. In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier ET, Logistic regressors LR, and Random Forests RF. The accuracy of the LR classifier 0.89 or 89 is higher than ET 82 and RF 82 classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve AUC, Recall, Precision, F1 score, Kappa, and MCC Matthews correlation coefficient. Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales 2019 to 2022. In order to evaluate the model graphically, ROC Receiver operating characteristic curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE t-distributed stochastic neighbour embedding plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

 Remote sensing RS, Geographic information systems GIS, and Machine learning can be integrated to predict land surface temperatures LST based on the data related to carbon monoxide CO, Formaldehyde HCHO, Nitrogen dioxide NO2, Sulphur dioxide SO2, absorbing aerosol index AAI, and Aerosol optical depth AOD. In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier ET, Logistic regressors LR, and Random Forests RF. The accuracy of the LR classifier 0.89 or 89 is higher than ET 82 and RF 82 classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve AUC, Recall, Precision, F1 score, Kappa, and MCC Matthews correlation coefficient. Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales 2019 to 2022. In order to evaluate the model graphically, ROC Receiver operating characteristic curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE t-distributed stochastic neighbour embedding plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Makale detayları

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Yazarlar:

Jagadish Kumar Mogaraju

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Land Management | Geospatial Information Systems and Geospatial Data Modelling | Geographical Information Systems (GIS) in Planning

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 2, pp.233-246

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323613

pISSN:

2548-0960

Machine learning

Geographic information systems

Sentinel-5-P

MODIS

Land surface temperature

Sayı

Makale detayları

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Yazarlar: Jagadish Kumar Mogaraju

Yayıncı: Murat YAKAR

Ana Başlıklar: Land Management | Geospatial Information Systems and Geospatial Data Modelling | Geographical Information Systems (GIS) in Planning

Dergi: International Journal of Engineering and Geosciences Vol.9 Issue 2, pp.233-246

Belge Tipi: Makale

Yayın Tarihi: 7/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001323613

pISSN: 2548-0960

Machine learning

Geographic information systems

Sentinel-5-P

MODIS

Land surface temperature

Sayı

Yazarlar:

Jagadish Kumar Mogaraju

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Land Management | Geospatial Information Systems and Geospatial Data Modelling | Geographical Information Systems (GIS) in Planning

Dergi:

International Journal of Engineering and Geosciences Vol.9 Issue 2, pp.233-246

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323613

pISSN:

2548-0960