2020
DOI: 10.1080/10106049.2020.1831621
|View full text |Cite
|
Sign up to set email alerts
|

Prediction of land cover change based on CA-ANN model to assess its local impacts on Bagerhat, southwestern coastal Bangladesh

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 20 publications
(3 citation statements)
references
References 28 publications
0
2
0
Order By: Relevance
“…The prediction module assumes that the land use change in the study area is in a stable development state during the study period. After the correction module determines the threshold and random parameter with the highest simulation accuracy, it executes the workflow of the correction module to predict the future land use change [ 66 , 67 ]. The integrated ANN-CA model is useful in areas such as land territorial spatial planning and suitability assessment since it can take into account all facets of the spatial system.…”
Section: Methodsmentioning
confidence: 99%
“…The prediction module assumes that the land use change in the study area is in a stable development state during the study period. After the correction module determines the threshold and random parameter with the highest simulation accuracy, it executes the workflow of the correction module to predict the future land use change [ 66 , 67 ]. The integrated ANN-CA model is useful in areas such as land territorial spatial planning and suitability assessment since it can take into account all facets of the spatial system.…”
Section: Methodsmentioning
confidence: 99%
“…Elevation, slope, distance from the settlement, distance from the road, distance from the river, and distance from the forest are considered in this research as potential transition determinants of future land cover change. ANN modeling is a reliable technique in many studies for land cover change (Lukas et al 2023;Rahman and Esha 2022;Saputra and Lee 2019). The ANN algorithm was run with a neighborhood rule of 1 px, learning rate of 0.001, maximum iterations of 1000, 10 hidden layers, and momentum of 0.050 (Khan and Sudheer 2022;Li et al 2017;Muhammad et al 2022;Perović et al 2018).…”
Section: Transitional Potential and Cellular Automata (Ca) Simulationmentioning
confidence: 99%
“…To model LULC changes, the type of machine learning adopted was ANN, based on the multilayer perceptron (MLP) network and the backpropagation algorithm. This is the most widely used model conformation in this type of work [19,20,22,34,35,[83][84][85]. Eight models named ANN1 to ANN8 were constructed (Table 3), based on experimentation with different parameterizations [16,20,34,86,87], and IBM SPSS 24 was used [88].…”
Section: Parameterization Of the Lulc Simulation Modelmentioning
confidence: 99%