An XGBoost Based Approach for Urban Land Use and Land Cover Change Modelling

Land use and land cover (LULC) change have significant consequences on habitat and environment. Scholars have developed several LULC models to identify the factors behind the changes and to simulate future LULC scenarios to assist in policymaking. Nevertheless, the accuracy of the models remains contentious and a matter of ongoing research agenda. Additionally, most of these studies used a training dataset to train the model and a validation dataset, which is a part of the original training dataset used to validate the model’s accuracy. However, to justify model’s actual predictive capability, we need to test the model on real-world dataset that was not used in modeling. So, we present XGBoost model to improve the accuracy of LULC prediction. Contrary to the typical studies, we use a separate test dataset to justify the model’s predictive capacity in real-world scenario. The result reveals that XGBoost model exhibits highest 84% kappa and 93% accuracy score compared to two benchmark model LR-CA (82% kappa and 92% accuracy score) and ANN-CA (82% kappa and 92% accuracy score). We also found that the built-up area increased by 48.7% in 2002 to 64% in 2010, while agricultural and vacant land declined by almost at the same magnitude over the period and the most important aspect of the LULC shift process in Khulna city was the proximity factors to major roads, industry and commercial establishments. The proposed model proved to increase the predictive accuracy making it much more reliable for analyzing and predicting urban LULC using spatial factors.