Document Type
Article
Publication Date
7-1-2022
Department
Department of Geological and Mining Engineering and Sciences
Abstract
Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simula-tions. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can in-corporate 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 coeffi-cients 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.
Publication Title
ISPRS International Journal of Geo-Information
Recommended Citation
Ahmad, W.,
Muhammad, K.,
Glass, H.,
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).
http://doi.org/10.3390/ijgi11070371
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16212
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/ijgi11070371