Comparing Four Machine Learning Algorithms for Land Cover Classification in Gold Mining: A Case Study of Kyaukpahto Gold Mine, Northern Myanmar

Abstract

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

Furthering Automatic Feature Extraction for Fit-for-Purpose Cadastral Updating: Cases from Peri-Urban Addis Ababa, Ethiopia

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Infiltration Efficiency Index for GIS Analysis Using Very-High-Spatial-Resolution Data

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Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

“…The results obtained in these studies show that machine learning techniques help significantly improve the accuracy in forest cover classification compared to traditional classification methods. Oo et al (2022) used machine learning algorithms, including RF, SVM, CART and maximum likelihood method in land cover classification of Kyaukpahto gold mine area, northern Myanmar from Landsat and Aster images. The overall accuracy value and Kappa index when classifying land cover using machine learning techniques are both higher than the maximum likelihood method, in which the RF algorithm achieving the highest accuracy (Oo et al, 2022).…”

“…Oo et al (2022) used machine learning algorithms, including RF, SVM, CART and maximum likelihood method in land cover classification of Kyaukpahto gold mine area, northern Myanmar from Landsat and Aster images. The overall accuracy value and Kappa index when classifying land cover using machine learning techniques are both higher than the maximum likelihood method, in which the RF algorithm achieving the highest accuracy (Oo et al, 2022). The effectiveness of the RF algorithm compared to other algorithms such as SVM, CART, ANN in classifying land cover/use from satellite images has also been proven in many studies in different regions of the world (Carrion & Southworth, 2018;Cheng & Wang, 2019;Volke & Abarca-Del-Rio, 2020;Mao et al, 2020;Yuh et al, 2023;Zhao et al, 2024).…”