Determination of rock depth using artificial intelligence techniques

被引：20
作者：
Viswanathan, R. [1 ]
Samui, Pijush [2 ]
机构：
[1] VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India
[2] VIT Univ, Ctr Disaster Mitigat & Management, Vellore 632014, Tamil Nadu, India
来源：
GEOSCIENCE FRONTIERS | 2016年 / 7卷 / 01期
关键词：
Rock depth; Spatial variability; Least square support vector machine; Gaussian process regression; Extreme learning machine;
D O I：
10.1016/j.gsf.2015.04.002
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
This article adopts three artificial intelligence techniques, Gaussian Process Regression (GPR), Least Square Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), for prediction of rock depth (d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques. Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth. (C) 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.
引用
收藏
页码：61 / 66
页数：6
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