Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh

Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh

Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in th...

Full description

Bibliographic Details
Main Authors: Akhter, S. (Author), Eibek, K.U (Author), Mallick, J. (Author), Talukdar, S. (Author), Towfiqul Islam, A.R.M (Author), Ziaul, S. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
agricultural land
Bangladesh
Couplings
decision making
Decision trees
dominance
Ensemble models
Fragmentation probability
GIS
Land use
Land use and land cover
Land use transitional matrix
Land-use and land cover
Learning algorithms
machine learning
Machine learning algorithm,
Machine learning algorithms,
Matrix algebra
precision
probability
Probability
Probability modelling
Random forests
Random subspaces
remote sensing
Remote sensing
River basins
Rivers
sensitivity analysis
Sensitivity analysis
software
Support vector machines
Teesta River
Teesta river basin
Teesta River basin
Vectors
Watersheds
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items