Back to Search
Start Over
Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
- Source :
- Open Geosciences, Vol 13, Iss 1, Pp 482-496 (2021)
- Publication Year :
- 2021
- Publisher :
- De Gruyter, 2021.
-
Abstract
- 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.
- Subjects :
- hot spring
deep learning
pretreatment
prediction
Geology
QE1-996.5
Subjects
Details
- Language :
- English
- ISSN :
- 23915447
- Volume :
- 13
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Open Geosciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0143093d740e43eab7a6b4684c51748b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1515/geo-2020-0237