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

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 informa...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2022-07, Vol.11 (7), p.371
Main Authors: Ahmed, Waqas, Muhammad, Khan, Glass, Hylke Jan, Chatterjee, Snehamoy, Khan, Asif, Hussain, Abid
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Coefficients
Computational efficiency
Computer applications
Computing time
Correlation coefficient
Correlation coefficients
Datasets
Decision making
Decision trees
Genetic algorithms
geostatistics
Lakes
Literature reviews
Machine learning
Machine Learning Algorithms (MLA)
Methods
Neural networks
Normal distribution
Ordinary Kriging (OK)
random forest (RF)
Regression analysis
SHAP
spatial estimation
Statistical analysis
Statistical methods
Variables
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi11070371