| contributor author | Pijush | |
| contributor author | Samui | |
| date accessioned | 2017-05-08T21:45:38Z | |
| date available | 2017-05-08T21:45:38Z | |
| date copyright | October 2013 | |
| date issued | 2013 | |
| identifier other | %28asce%29gm%2E1943-5622%2E0000265.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/61654 | |
| description abstract | This article adopts two data mining techniques, support vector machine (SVM) and least-squares support vector machine (LSSVM), for prediction of soil electrical resistivity based on soil properties and thermal resistivity. Two models (Model I and Model II) are developed. Model I uses the percentage sum of the gravel-size and sand-size fractions (%) and thermal resistivity ( | |
| publisher | American Society of Civil Engineers | |
| title | Applicability of Data Mining Techniques for Predicting Electrical Resistivity of Soils Based on Thermal Resistivity | |
| type | Journal Paper | |
| journal volume | 13 | |
| journal issue | 5 | |
| journal title | International Journal of Geomechanics | |
| identifier doi | 10.1061/(ASCE)GM.1943-5622.0000253 | |
| tree | International Journal of Geomechanics:;2013:;Volume ( 013 ):;issue: 005 | |
| contenttype | Fulltext | |
