Prediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach

Prediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach

Zeena Adil Najeeb

Contact

Nahrain University

Prediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach

Article Fingerprint

ReserarchID

71V66

Prediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

The objective of this paper is to assess the applicability and performance of multivariate adoptive regression spline analysis (MARS) for prediction elevation height in digital elevation model. MARS is an adaptive, nonparametric regression approach. Three dimensional coordinates (X, Y, and Z) in Equal-Sized grid Cell observed and recognized vie Differential Global Positioning System (DGPS) at AL-Nahrain university site. Mathematical prediction models with their errors and analysis are established in this paper. As the same time the independent variables X,Y and the dependent predicted variable Z the height which be used in. The data were dividedrandomly into training and testing70% of the entire data set is utilized for training and the remaining30% for testing. MARS depends on two steps for computation logarithm forward and backward to get better performance MARS adopts Generalized Cross-Validation (GCV) with different statistical parameters of standard deviation, root mean square error and residuals.

References

15 Cites in Article

Reference Format

Vincent Chaplot,Frédéric Darboux,Hocine Bourennane,Sophie Leguédois,Norbert Silvera,Konngkeo Phachomphon (2006). Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density.
W Shi,Y Tian (2006). A hybrid interpolation method for the refinement of a regular grid digital elevation model.
P Arun (2013). A comparative analysis of different DEM interpolation methods.
O Okwuashi,C Ndehedehe (2015). Digital terrain model height estimation using support vector machine regression.
H Karaborka,O Baykanb,C Altuntasa,F Yildza (null). Estimation of Melting Temperature of Molecular Cocrystals Using Artificial Neural Network Model.
Zhilin Li,Qing Zhu,Christopher Gold (2005). Beyond Digital Terrain Modeling.
David Merwin,Robert Cromley,L Daniel,Civco (2002). Artificial Neural Networks as a Method of Spatial Interpolation for Digital Elevation Models.
Yuming Zhou,Hareton Leung (2007). Predicting object-oriented software maintainability using multivariate adaptive regression splines.
Jianya Gong,Qing Zhu,Yaolinliu (2005). DEM interpolation based on artificial neural networks.
Wu Aman,Y Y Beiping Ziggah (2013). Testing Simple Regression Model for Coordinate Tranformation by Comparing Its Predictive Result for two Regions.
Bashar Tarawneh (2017). Predicting standard penetration test N-value from cone penetration test data using artificial neural networks.
Sonja Sekulic,Bruce Kowalski (1992). MARS: A tutorial.
Peter Craven,Grace Wahba (1979). Smoothing noisy data with spline functions.
Jerome Friedman (1991). Multivariate Adaptive Regression Splines.
Dwarkadas Pijushsamui,Pralhaddas Kothari2 A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus (Gmax) and Minimum Damping Ratio (εmin).

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Zeena Adil Najeeb. 2017. \u201cPrediction of Digital Elevation Model Height by Multivariate Adaptive Regression Splines (Mars) Interpolation Approach\u201d. Global Journal of Research in Engineering - E: Civil & Structural GJRE-E Volume 17 (GJRE Volume 17 Issue E3): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJRE Volume 17 Issue E3
Pg. 31- 37

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords

Not Found

Classification

GJRE-E Classification: FOR Code: 090599

Submission ReceivedDecember 14, 2016
Peer Review Double Blind
Handling Editor
Accepted January 1, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

December 18, 2017

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 101

Country: Iraq

Subject: Global Journal of Research in Engineering - E: Civil & Structural

Authors: Zeena Adil Najeeb (PhD/Dr. count: 0)

View Count (all-time): 226

Total Views (Real + Logic): 3486

Total Downloads (simulated): 1636

Publish Date: 2017 12, Mon

Monthly Totals (Real + Logic):

Month 1: 53 views
Month 2: 33 views
Month 3: 53 views
Month 4: 36 views
Month 5: 61 views
Month 6: 37 views
Month 7: 40 views
Month 8: 41 views
Month 9: 34 views
Month 10: 45 views
Month 11: 49 views
Month 12: 45 views
Month 13: 41 views
Month 14: 34 views
Month 15: 36 views
Month 16: 21 views
Month 17: 23 views
Month 18: 37 views
Month 19: 36 views
Month 20: 21 views
Month 21: 40 views
Month 22: 29 views
Month 23: 31 views
Month 24: 47 views
Month 25: 29 views
Month 26: 36 views
Month 27: 35 views
Month 28: 37 views
Month 29: 18 views
Month 30: 45 views
Month 31: 42 views
Month 32: 24 views
Month 33: 16 views
Month 34: 27 views
Month 35: 37 views
Month 36: 42 views
Month 37: 35 views
Month 38: 45 views
Month 39: 19 views
Month 40: 39 views
Month 41: 31 views
Month 42: 33 views
Month 43: 26 views
Month 44: 28 views
Month 45: 12 views
Month 46: 34 views
Month 47: 35 views
Month 48: 18 views
Month 49: 25 views
Month 50: 31 views
Month 51: 29 views
Month 52: 31 views
Month 53: 26 views
Month 54: 34 views
Month 55: 33 views
Month 56: 40 views
Month 57: 28 views
Month 58: 20 views
Month 59: 39 views
Month 60: 36 views
Month 61: 43 views
Month 62: 37 views
Month 63: 35 views
Month 64: 42 views
Month 65: 40 views
Month 66: 14 views
Month 67: 35 views
Month 68: 29 views
Month 69: 31 views
Month 70: 37 views
Month 71: 23 views
Month 72: 38 views
Month 73: 32 views
Month 74: 28 views
Month 75: 31 views
Month 76: 23 views
Month 77: 44 views
Month 78: 22 views
Month 79: 19 views
Month 80: 33 views
Month 81: 28 views
Month 82: 40 views
Month 83: 33 views
Month 84: 13 views
Month 85: 34 views
Month 86: 22 views
Month 87: 34 views
Month 88: 32 views
Month 89: 29 views
Month 90: 31 views
Month 91: 28 views
Month 92: 35 views
Month 93: 41 views
Month 94: 23 views
Month 95: 30 views
Month 96: 48 views
Month 97: 34 views
Month 98: 29 views
Month 99: 32 views
Month 100: 27 views

Total Views: 3486

Total Downloads: 1636

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]