APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

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11. LULC changes to riverine flooding: A case study on the Jamuna River, Bangladesh using the multilayer perceptron model M. Hasan et al. 10.1016/j.rineng.2023.101079
12. Adoption of Machine Learning Technique in Nile River Islands Classification N. Kamal et al. 10.2166/hydro.2022.154
13. Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data T. Ramachandra et al. 10.1007/s42452-023-05496-4
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18. Classification of land use/land cover using artificial intelligence (ANN-RF) E. Alshari et al. 10.3389/frai.2022.964279
19. Multilayer perceptron and Markov Chain analysis based hybrid-approach for predicting land use land cover change dynamics with Sentinel-2 imagery H. Abbas et al. 10.1080/10106049.2023.2256297
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