A data-driven machine learning-based approach for urban land cover change modeling: A case of Khulna City Corporation area
Category:- Journal; Year:- 2021
Discipline:- Urban and Rural Planning Discipline
School:- Science, Engineering & Technology School
Abstract
Land use and land cover (LULC) changes have significant consequences on
habitat and the environment. Past studies developed several LULC change models
to identify the factors behind the changes and to simulate future LULC
scenarios. However, the accuracy of these models remained contentious and a
matter of ongoing debate and research. Most of these studies used a training
dataset to train the model and a validation dataset to validate the prediction
accuracy, both of which are a part of the original training dataset. However,
to evaluate the model's actual predictive capability in terms of spatial data
modeling, it is necessary to test the model's performance on the real-world
dataset. In this study, we presented an XGBoost model aiming at improving the
prediction accuracy while used a separate test dataset to test the model's
actual predictive capacity. We applied the method to predict the land cover
change of the Khulna City Corporation (KCC) area in Bangladesh. The study
reveals that the KCC area experienced rapid urban development during the
2002–2018 period while the agricultural and vacant land declined at a similar
rate. The major factors contributing to this substantial change of the city's
land covers are-proximity to existing built-up areas and proximity to major
roads. Our study indicates that agricultural areas and wetlands closer to the
major roads and existing urban areas have a greater probability of converting
into built-up areas. Our experiment demonstrates that the XGBoost model can
predict the city's land cover change with greater accuracy and outperforms the
benchmark models such as the LR-CA and ANN-CA. The finding assures the
reliability of the XGBoost model while predicting future land-cover scenarios.