Machine learning assessment of illegal mining (Galamsey) impacts on forest vegetation: a case study of Wassa Amenfi East District, Ghana
Yazarlar (16)
Clement Kwang University Of Ghana, Gana
Ian Kofi Afele University Of Ghana, Gana
Emmanuel Yeboah Nanjing University Of Information Science & Technology, Çin
Isaac Sarfo
Henan University, Çin
Henan University, Çin
Michael Batame University Of Georgia, Amerika Birleşik Devletleri
Abraham Okrah Nanjing University Of Information Science & Technology, Çin
Myint Myint Shwe Nanjing University Of Information Science & Technology, Çin
Williams Siaw Nanjing University Of Information Science & Technology, Çin
Dinah Boyetey Nanjing University Of Information Science & Technology, Çin
Richard Odoi Larbi University Of Mines And Technology, Gana
Augustine O. K. N. Mensah Nanjing University Of Information Science & Technology, Çin
Charafa El Rhadiouini Nanjing University Of Information Science & Technology, Çin
Ali Hasan Jaffry Nanjing University Of Information Science & Technology, Çin
Fareeha Siddique Nanjing University Of Information Science & Technology, Çin
Rukhshinda Aftab Nanjing University Of Information Science & Technology, Çin
Doç. Dr. Öznur IŞINKARALAR Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Earth Science Informatics (Q2) | ||
| Dergi ISSN | 1865-0473 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2025 |
| Cilt / Sayı / Sayfa | 18 / 2 / 348–0 | DOI | 10.1007/s12145-025-01860-7 |
| Makale Linki | https://doi.org/10.1007/s12145-025-01860-7 | ||
| UAK Araştırma Alanları |
İklim Değişikliği ve Planlama
Planlamada Sürdürülebilirlik, Dayanıklılık, Ekoloji ve Enerji
Şehir Planlama
|
||
| Özet |
| This study addresses the persistent land management challenge of galamsey (illegal mining) in Wassa Amenfi East by exploring the impact of these activities on vegetation through advanced machine learning techniques. A comparative analysis was conducted using four machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Maximum Likelihood Classification (MLC) to assess their effectiveness in detecting and analyzing vegetation changes due to galamsey operations. The results highlight the Random Forest (RF) algorithm as the most effective, with overall accuracy scores of 88% for 2015 and 87% for 2023, and Kappa Coefficient values of 0.84 and 0.82, respectively, demonstrating its consistent superiority over the other methods. Findings of the study reveal significant vegetation and forest cover loss due to galamsey, driven by poverty … |
| Anahtar Kelimeler |
| Artificial neural network | Galamsey | Machine learning techniques | Maximum likelihood classification | Random | Support vector machine |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 3 |
| Google Scholar | 5 |