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

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

Content Provider	MDPI
Author	Ahmed, Waqas Muhammad, Khan Glass, Hylke Jan Chatterjee, Snehamoy Khan, Asif Hussain, Abid
Copyright Year	2022
Description	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.
Starting Page	371
e-ISSN	22209964
DOI	10.3390/ijgi11070371
Journal	ISPRS International Journal of Geo-Information
Issue Number	7
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2022-07-01
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Information and Library Science Ordinary Kriging (ok) Random Forest (rf) Machine Learning Algorithms (mla) Geostatistics Spatial Estimation Shap Interpretable Machine Learning
Content Type	Text
Resource Type	Article

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