Next Article in Journal
Perspectives on “Earth Observation and GIScience for Agricultural Applications”
Previous Article in Journal
Mapping Urban Forms Worldwide: An Analysis of 8910 Street Networks and 25 Indicators
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJGI
Volume 11
Issue 7
10.3390/ijgi11070371
ijgi-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

by
Waqas Ahmed
1,
Khan Muhammad
1,2,*,
Hylke Jan Glass
3,
Snehamoy Chatterjee
4,
Asif Khan
1,5 and
Abid Hussain
1
1
Intelligent Information Processing Lab, National Centre of Artificial Intelligence, University of Engineering and Technology, Peshawar 25000, Pakhtunkhwa, Pakistan
2
Department of Mining Engineering, University of Engineering and Technology, Peshawar 25000, Pakhtunkhwa, Pakistan
3
Minerals Engineering Research Group, Camborne School of Mines, University of Exeter, Penryn, Cornwall TR10 9FE, UK
4
Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931, USA
5
Department of Mineral Resource Engineering, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur 22621, Pakhtunkhwa, Pakistan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2022, 11(7), 371; https://doi.org/10.3390/ijgi11070371
Submission received: 19 May 2022 / Revised: 24 June 2022 / Accepted: 26 June 2022 / Published: 1 July 2022
Browse Figures
Versions Notes

Abstract

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can incorporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p < 10 × 10−5), with correlation coefficients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency.
Keywords: Ordinary Kriging (OK); random forest (RF); Machine Learning Algorithms (MLA); geostatistics; spatial estimation; SHAP; interpretable machine learning Ordinary Kriging (OK); random forest (RF); Machine Learning Algorithms (MLA); geostatistics; spatial estimation; SHAP; interpretable machine learning

Share and Cite

MDPI and ACS Style

Ahmed, W.; Muhammad, K.; Glass, H.J.; Chatterjee, S.; Khan, A.; Hussain, A. Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK. ISPRS Int. J. Geo-Inf. 2022, 11, 371. https://doi.org/10.3390/ijgi11070371

AMA Style

Ahmed W, Muhammad K, Glass HJ, Chatterjee S, Khan A, Hussain A. Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK. ISPRS International Journal of Geo-Information. 2022; 11(7):371. https://doi.org/10.3390/ijgi11070371

Chicago/Turabian Style

Ahmed, Waqas, Khan Muhammad, Hylke Jan Glass, Snehamoy Chatterjee, Asif Khan, and Abid Hussain. 2022. "Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK" ISPRS International Journal of Geo-Information 11, no. 7: 371. https://doi.org/10.3390/ijgi11070371

APA Style

Ahmed, W., Muhammad, K., Glass, H. J., Chatterjee, S., Khan, A., & Hussain, A. (2022). Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK. ISPRS International Journal of Geo-Information, 11(7), 371. https://doi.org/10.3390/ijgi11070371

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop