A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction
| Autor: | T. Fikret Kurnaz, Yılmaz Kaya |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Global and Planetary Change
Earthquake engineering Artificial neural network Ensemble forecasting Group method of data handling Effective stress 0208 environmental biotechnology Soil Science Liquefaction Geology 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Pollution 020801 environmental engineering Environmental Chemistry Data mining Soil liquefaction computer Predictive modelling 0105 earth and related environmental sciences Earth-Surface Processes Water Science and Technology |
| Zdroj: | Environmental Earth Sciences. 78 |
| ISSN: | 1866-6299 1866-6280 |
| DOI: | 10.1007/s12665-019-8344-7 |
| Popis: | This study presents a novel ensemble group method of data handling (EGMDH) model based on classification for the prediction of liquefaction potential of soils. Liquefaction is one of the most complex problems in geotechnical earthquake engineering. The database used in this study consists of 212 CPT-based field records from eight major earthquakes. The input parameters are selected as cone tip resistance, total and effective stress, penetration depth, max peak horizontal acceleration and earthquake magnitude for the prediction models. The proposed EGMDH model results were also compared to the other classifier models, particularly the results of the group method of data handling (GMDH) model. The results of this study indicated that the proposed EGMDH model has achieved more successful results on the prediction of the liquefaction potential of soils compared to the other classifier models by improving the prediction performance of the GMDH model. |
| Databáze: | OpenAIRE |
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