COMBATING ILLEGAL SMALL-SCALE MINING (GALAMSEY) IN GHANA WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE APPROACH

Galamsey, also known as illegal small-scale mining, persists in causing significant damage to Ghana's environment, including water bodies, agricultural areas, and public health. The present study introduces a comprehensive approach that utilizes Artificial Intelligence (AI) technology to promptly identify, oversee, and forecast illicit mining operations. By employing satellite imaging, drone monitoring, predictive analytics, and machine learning models, we propose a scalable and effective method to reduce the environmental and economic impact of galamsey. A methodology is presented that describes the development and execution of an artificial intelligence system utilizing actual data from high-risk mining areas in Ghana. The present study provides empirical evidence supporting the efficacy of artificial intelligence (AI) in facilitating real-time surveillance and forecasting forthcoming illicit mining operations. Furthermore, we address possible obstacles, including technical constraints, privacy issues, and the scarcity of proficient staff, and provide suggestions for surmounting these hindrances.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-04-01
Primary Topic
Magnetic confinement fusion research
Type
article
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article

COMBATING ILLEGAL SMALL-SCALE MINING (GALAMSEY) IN GHANA WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE APPROACH

SHILAP Revista de lepidopterología
Magnetic confinement fusion research
article

COMBATING ILLEGAL SMALL-SCALE MINING (GALAMSEY) IN GHANA WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE APPROACH

article en

Abstract

Galamsey, also known as illegal small-scale mining, persists in causing significant damage to Ghana's environment, including water bodies, agricultural areas, and public health. The present study introduces a comprehensive approach that utilizes Artificial Intelligence (AI) technology to promptly identify, oversee, and forecast illicit mining operations. By employing satellite imaging, drone monitoring, predictive analytics, and machine learning models, we propose a scalable and effective method to reduce the environmental and economic impact of galamsey. A methodology is presented that describes the development and execution of an artificial intelligence system utilizing actual data from high-risk mining areas in Ghana. The present study provides empirical evidence supporting the efficacy of artificial intelligence (AI) in facilitating real-time surveillance and forecasting forthcoming illicit mining operations. Furthermore, we address possible obstacles, including technical constraints, privacy issues, and the scarcity of proficient staff, and provide suggestions for surmounting these hindrances.

SHILAP Revista de lepidopterología
University of Greater Manchester (GB), National Insurance Institute of Israel (IL), Regent University College of Science and Technology (GH)
Openalex Percentile: Top 71%
Magnetic confinement fusion research
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COMBATING ILLEGAL SMALL-SCALE MINING (GALAMSEY) IN GHANA WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE APPROACH · SHILAP Revista de lepidopterología (2025) | TGRS Research Map | TGRS