| 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 |
|
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 |
| 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, unemployment, poor policies, livelihood pursuits, and quick financial gains by traditional authorities. This study stresses the need for targeted interventions to mitigate the environmental impact of galamsey and suggests the adoption of advanced machine learning techniques for more accurate and effective land management strategies. |
| Anahtar Kelimeler |
| Artificial neural network | Galamsey | Machine learning techniques | Maximum likelihood classification | Random | Support vector machine |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Earth Science Informatics |
| Dergi ISSN | 1865-0473 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2025 |
| Cilt No | 18 |
| Sayı | 2 |
| Sayfalar | 348 / 0 |
| Doi Numarası | 10.1007/s12145-025-01860-7 |
| Makale Linki | https://doi.org/10.1007/s12145-025-01860-7 |
| Atıf Sayıları | |
| WoS | 2 |
| SCOPUS | 2 |
| Google Scholar | 3 |
