Modeling mining-induced land degradation in Itagunmodi: A multi-temporal machine learning approach with random forest and gradient boosting

Modeling mining-induced land degradation in Itagunmodi: A multi-temporal machine learning approach with random forest and gradient boosting

Mining activities significantly impact LULC (LULC) dynamics, often leading to environmental degradation and socio-economic consequences. This study employs a multi-temporal machine learning approach using Smile Random Forest (SRF) and Smile Gradient Tree Boost (SGTB) models to analyze and predict mi...

Full description

Saved in:
Bibliographic Details
Main Authors: Johnson Ayomide Ibukun, Ayomide Emmanuel Olubaju, Samson Favour Thomas, Esther Omotolani Sodipo, Sehinde Ayoola Akinbiola, Saheed Oyekunle Oyetunji, Kasye Shitu, Dmitry E. Kucher, Aqil Tariq
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Trees, Forests and People
Subjects:
Mining-induced land degradation
LULC change
Machine learning classification
Remote sensing analysis
Sustainable land management
Online Access:http://www.sciencedirect.com/science/article/pii/S2666719325001529
Tags: Add Tag
No Tags, Be the first to tag this record!
Login for hold and recall information

Internet

http://www.sciencedirect.com/science/article/pii/S2666719325001529

Similar Items