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

Rahman, Mohd Tadza Abdul; Esha, Eshrat Jahan

doi:10.1080/10106049.2020.1831621

Cited by 20 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

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 suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

Nie

2024

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

Nie

2024

Heliyon

View full text Add to dashboard Cite

“…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%

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

Iskandar,

Saidah,

Kurnia

et al. 2024

J. Sylva Lestari

View full text Add to dashboard Cite

A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

show abstract

“…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%

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

et al. 2022

View full text Add to dashboard Cite

The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.

show abstract

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

Cited by 20 publications

References 28 publications

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

Contact Info

Product

Resources

About