Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning

The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a bla...

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Autores principales:	Sang Xuejia, Xue Linfu, Li Xiaoshun
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	hot spring deep learning pretreatment prediction Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/0143093d740e43eab7a6b4684c51748b
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id	oai:doaj.org-article:0143093d740e43eab7a6b4684c51748b
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spelling	oai:doaj.org-article:0143093d740e43eab7a6b4684c51748b2021-12-05T14:10:48ZConsidering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning2391-544710.1515/geo-2020-0237https://doaj.org/article/0143093d740e43eab7a6b4684c51748b2021-04-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0237https://doaj.org/toc/2391-5447The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.Sang XuejiaXue LinfuLi XiaoshunDe Gruyterarticlehot springdeep learningpretreatmentpredictionGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 482-496 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hot spring deep learning pretreatment prediction Geology QE1-996.5
spellingShingle	hot spring deep learning pretreatment prediction Geology QE1-996.5 Sang Xuejia Xue Linfu Li Xiaoshun Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
description	The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.
format	article
author	Sang Xuejia Xue Linfu Li Xiaoshun
author_facet	Sang Xuejia Xue Linfu Li Xiaoshun
author_sort	Sang Xuejia
title	Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
title_short	Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
title_full	Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
title_fullStr	Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
title_full_unstemmed	Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
title_sort	considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/0143093d740e43eab7a6b4684c51748b
work_keys_str_mv	AT sangxuejia consideringthegeologicalsignificanceindatapreprocessingandimprovingthepredictionaccuracyofhotspringsbydeeplearning AT xuelinfu consideringthegeologicalsignificanceindatapreprocessingandimprovingthepredictionaccuracyofhotspringsbydeeplearning AT lixiaoshun consideringthegeologicalsignificanceindatapreprocessingandimprovingthepredictionaccuracyofhotspringsbydeeplearning
_version_	1718371699319111680

Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning

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