relation: https://khub.utp.edu.my/scholars/4268/
title: Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression
creator: Abdulkarim, M.
creator: Ahmad, W.F.W.
creator: Ansari, A.
creator: Nyamasvisva, E.T.
creator: Shafie, A.
description: In this study, a Multi-Layer Perceptron Neural Network and Multiple Regression techniques are used to estimate airwaves associated with shallow water Controlled-Source Electro-Magnetic (CSEM) data. Both techniques are appropriate for the development of estimation models. However, multiple regression models make some assumptions about the underlying data. These assumptions include independence, normality and homogeneity of variance. Conversely, neural network based models are not constrained by such assumptions. The performance of the two techniques is calculated based on coefficient of determination (R2) and mean square error (MSE). The results indicate that MLP produced better estimate for the airwaves with MSE of 0.0113 and R2 of 0.9935. © 2014 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2014
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Abdulkarim, M. and Ahmad, W.F.W. and Ansari, A. and Nyamasvisva, E.T. and Shafie, A.  (2014) Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938772012&doi=10.1109%2fICCOINS.2014.6868367&partnerID=40&md5=3ac022e89cf2d912ca20f73818e47b26
relation: 10.1109/ICCOINS.2014.6868367
identifier: 10.1109/ICCOINS.2014.6868367