Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area

Abstract

Future prediction modeling of land use/land cover (LULC) is crucial for coastal regions due to unique challenges and vulnerabilities associated with these areas. This research aims to evaluate the performance of three models: Logistic Regressor (LR), Artificial Neural Network (ANN), and Weight of Evidence (CA-WoE) combined with Cellular Automata (CA) to predict the future LULC change for the Sundarbans coastal area, straddling the international boundary of Bangladesh and India. Hybrid models of CA-LR, CA-ANN, and CA-WoE were applied to predict LULC changes for the Sundarbans deltaic region for 2030 and 2050, based on a host of spatial and environmental variables. The supervised machine learning algorithm of the Random Forest (RF) classification model was used to classify satellite images during 1990–2020 to create LULC maps. Image classification was based on four distinct LULC classes: (i) mangroves, (ii) waterbodies, (iii) built-up, and (iv) barren land. Respective percentage correctness (PCN) for model types of CA-LR, CA-ANN, and CA-WoE was 90.82%, 92.06%, and 92.12% with overall Cohen's kappa metrics (OKM) of 0.815, 0.841, and 0.842, respectively. Overall, CA-WoE model performance was superior to CA-ANN and CA-LR models in predicting LULC maps for 2030 and 2050. Model results suggest that future mangrove forest area in the Sundarbans will decrease, and waterbodies will increase in area by 2050. The findings of this study may guide future sustainable land use management in the Sundarbans. This study also provides model selection techniques for predicting coastal LULC changes worldwide.