Investigation of artificial neural network performance in the aerosol properties retrieval
Aerosols are an integral part of Earth's climate system and their effect on climate makes this field a relevant research problem. The artificial neural network (ANN) technique is an upcoming technique in different research fields. In the current work, we have evaluated the performance of an ANN...
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Online Access: | https://doi.org/10.2166/wcc.2021.336 https://doaj.org/article/f1833868710d4a6fbd5c6c1abbcf01ed |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:f1833868710d4a6fbd5c6c1abbcf01ed 2023-05-15T13:06:42+02:00 Investigation of artificial neural network performance in the aerosol properties retrieval Nishi Srivastava D. Vignesh Nisheeth Saxena 2021-09-01 https://doi.org/10.2166/wcc.2021.336 https://doaj.org/article/f1833868710d4a6fbd5c6c1abbcf01ed en eng IWA Publishing 2040-2244 2408-9354 doi:10.2166/wcc.2021.336 https://doaj.org/article/f1833868710d4a6fbd5c6c1abbcf01ed undefined Journal of Water and Climate Change, Vol 12, Iss 6, Pp 2814-2834 (2021) aeronet aerosols aod artificial neural network geo info Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.2166/wcc.2021.336 2023-01-22T19:12:25Z Aerosols are an integral part of Earth's climate system and their effect on climate makes this field a relevant research problem. The artificial neural network (ANN) technique is an upcoming technique in different research fields. In the current work, we have evaluated the performance of an ANN with its parameters in simulating the aerosol's properties. ANN evaluation is performed over three sites (Kanpur, Jaipur, and Gandhi College) in the Indian region. We evaluated the performance of ANN for model's hyperparameter (number of hidden layers) and optimizer's hyperparameters (learning rate and number of iterations). The optical properties of aerosols from AERONET (AErosol RObotic NETwork) are used as input to ANN to estimate the aerosol optical depth (AOD) and Angstrom exponent. Results emphasized the need for optimal learning rate values and the number of iterations to get accurate results with low computational cost and to avoid overfitting. We observed a 23–25% increase in computational time with an increase in iteration. Thus, a meticulous selection of these parameters should be made for accurate estimations. The result indicates that the developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations. HIGHLIGHTS In designing an ANN, we must choose the optimal number of iterations based on computational cost and quality of results.; Our finding indicates that ANN with more hidden layers can perform reasonably well at a low number of iterations.; The specific site may need a different set of hyperparameters for the best performance of the ANN.; The developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations; Article in Journal/Newspaper Aerosol Robotic Network Unknown Indian Journal of Water and Climate Change 12 6 2814 2834 |
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language |
English |
topic |
aeronet aerosols aod artificial neural network geo info |
spellingShingle |
aeronet aerosols aod artificial neural network geo info Nishi Srivastava D. Vignesh Nisheeth Saxena Investigation of artificial neural network performance in the aerosol properties retrieval |
topic_facet |
aeronet aerosols aod artificial neural network geo info |
description |
Aerosols are an integral part of Earth's climate system and their effect on climate makes this field a relevant research problem. The artificial neural network (ANN) technique is an upcoming technique in different research fields. In the current work, we have evaluated the performance of an ANN with its parameters in simulating the aerosol's properties. ANN evaluation is performed over three sites (Kanpur, Jaipur, and Gandhi College) in the Indian region. We evaluated the performance of ANN for model's hyperparameter (number of hidden layers) and optimizer's hyperparameters (learning rate and number of iterations). The optical properties of aerosols from AERONET (AErosol RObotic NETwork) are used as input to ANN to estimate the aerosol optical depth (AOD) and Angstrom exponent. Results emphasized the need for optimal learning rate values and the number of iterations to get accurate results with low computational cost and to avoid overfitting. We observed a 23–25% increase in computational time with an increase in iteration. Thus, a meticulous selection of these parameters should be made for accurate estimations. The result indicates that the developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations. HIGHLIGHTS In designing an ANN, we must choose the optimal number of iterations based on computational cost and quality of results.; Our finding indicates that ANN with more hidden layers can perform reasonably well at a low number of iterations.; The specific site may need a different set of hyperparameters for the best performance of the ANN.; The developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations; |
format |
Article in Journal/Newspaper |
author |
Nishi Srivastava D. Vignesh Nisheeth Saxena |
author_facet |
Nishi Srivastava D. Vignesh Nisheeth Saxena |
author_sort |
Nishi Srivastava |
title |
Investigation of artificial neural network performance in the aerosol properties retrieval |
title_short |
Investigation of artificial neural network performance in the aerosol properties retrieval |
title_full |
Investigation of artificial neural network performance in the aerosol properties retrieval |
title_fullStr |
Investigation of artificial neural network performance in the aerosol properties retrieval |
title_full_unstemmed |
Investigation of artificial neural network performance in the aerosol properties retrieval |
title_sort |
investigation of artificial neural network performance in the aerosol properties retrieval |
publisher |
IWA Publishing |
publishDate |
2021 |
url |
https://doi.org/10.2166/wcc.2021.336 https://doaj.org/article/f1833868710d4a6fbd5c6c1abbcf01ed |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Journal of Water and Climate Change, Vol 12, Iss 6, Pp 2814-2834 (2021) |
op_relation |
2040-2244 2408-9354 doi:10.2166/wcc.2021.336 https://doaj.org/article/f1833868710d4a6fbd5c6c1abbcf01ed |
op_rights |
undefined |
op_doi |
https://doi.org/10.2166/wcc.2021.336 |
container_title |
Journal of Water and Climate Change |
container_volume |
12 |
container_issue |
6 |
container_start_page |
2814 |
op_container_end_page |
2834 |
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1766016870725451776 |