Geospatial machine learning model for limestone suitability assessment

Abstract

• Limestone classification enhanced using ML and GIS integration. • Five ML classifiers modeled geochemical and thickness data. • Random Forest showed best precision-recall and predictive strength. • LSI map outlined high-potential CaO-rich limestone zones. Accurate assessment of limestone quality and investment potential requires advanced techniques due to spatial variability in thickness and geochemical composition which traditional exploration methods cannot capture efficiently. This study integrates Machine Learning (ML) and Geographic Information Systems (GIS) to optimize limestone exploration. The analysis used limestone and overburden thickness, X-ray Fluorescence (XRF), and Atomic Absorption Spectroscopy (AAS) data from 23 core samples with multiple ML classifiers: Decision Tree (DT), Logistic Regression (LR), XGBoost, Support Vector Machine (SVM), and Random Forest (RF). Predictive ability was evaluated using Accuracy, F1 Scores, Precision-Recall (P-R) curves, Area under P-R curves (AUC-PR), and Feature importance Analysis. Features were validated using a non-parametric approach. Predicted datasets of the selected classifier were subjected to limestone classification criteria and integrated into a GIS to generate predictive Limestone Suitability Index (LSI) map. DT and LR models showed 100 % accuracy, XGBoost performed poorly at 60 %, and SVM and RF had moderate performance (80 %). The F1-scores of 1.00 for LR and DT, 0.71 for SVM and RF, and 0.45 for XGBoost indicate prediction reliability differences. RF and SVM achieved balanced precision-recall (0.65–0.80), with RF attaining a higher AUC_PR (0.871) than SVM (0.643). The non-parametric validation of the features identified RF as most suitable. The LSI map based on RF outputs, categorized the area into high, medium, and low potential zones with high potential zones characterized by thick, CaO rich limestone beds (16.0–34.1 m, CaO ≥ 50 %, SiO2 ≤ 8 %). This made ML, specifically, RF an essential tool for limestone resource evaluation.