Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms

To maintain sustainability in the development, measured the yearly change rate of the land through Land Cover classified maps that hold the data which is surveyed as an influential factor for environment management and urbanization. This paper measured the change rate, which is helpful for the management of the city to define the new policy and implement the best one to maintain the natural resources. Machine Learning algorithms are utilized to produce the most acknowledged Land Cover maps using the GEE cloud-based reliable platform using the LANDSAT8 satellite imagery. For the classification used the Random Forest (RF) and Support Vector Machine (SVM) Algorithm. This investigation also found that the Support Vector Machine (SVM) classifier accomplished better over-all accuracy and Kappa coefficient as compared to the Random Forest (RF) classifier while the training sample for both is the same.

@article{Hassan2020,
  title     = {Urbanization Change Analysis based on SVM and RF Machine Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Farhad Hassan and Tauqeer Safdar and Ghulam Irtaza and Aman Ullah Khan and Syed Muhammad Husnain Kazmi and Farah Murtaza},
  doi       = {10.14569/IJACSA.2020.0110573},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110573}
}