Feasibility analysis of extreme learning machine for predicting thermal conductivity of rocks
| العنوان: | Feasibility analysis of extreme learning machine for predicting thermal conductivity of rocks |
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| المؤلفون: | Jianguo Kang, Shaohua Wu, Ping Gao, Ziwang Yu, Yanjun Zhang |
| المصدر: | Environmental Earth Sciences. 80 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Global and Planetary Change, Coefficient of determination, Mean squared error, business.industry, Geothermal energy, 0208 environmental biotechnology, Soil Science, Geology, Soil science, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Pollution, 020801 environmental engineering, Support vector machine, Thermal conductivity, Environmental Chemistry, business, Porosity, Geothermal gradient, 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, Extreme learning machine |
| الوصف: | In the development and utilization process of geothermal energy, the thermal conductivity of the rock plays a key role in engineering design. Potentials for further improvement of the geothermal engineering design lie in the improvement of the accuracy of thermal conductivity model. For the prediction of the thermal conductivity of rocks, emerging extreme learning machine (ELM) methods could prove to be highly accurate and efficient new methods. In this paper, the thermal conductivity of various rocks in the Songliao Basin (China) was measured by thermal conductivity scanning (TCS), and 101 sets of data were obtained. The correlation between porosity, moisture content, density, P-wave velocity and the thermal conductivity was analyzed. The results reveal that four parameters are suitable as input variables for predicting the thermal conductivity. Small-sampling prediction models were created using a new ELM-based approach. To demonstrate the model performance, seven prediction models were developed using ELM, support vector regression (SVR) and back propagation neural network (BPNN) algorithms, and theoretical models. The performance of seven models was compared by mean square error (MSE) and coefficient of determination (R2). The results show that the ELM-based model has better operating speed and forecasting accuracy, and good overall generalization performance in predicting rock thermal conductivity, which can provide accurate data in time for engineering application. |
| تدمد: | 1866-6299 1866-6280 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::089bc960a7953b2e2494251051964a8b https://doi.org/10.1007/s12665-021-09745-w |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........089bc960a7953b2e2494251051964a8b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18666299 18666280 |
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