Hybrid Intelligence and Explainable AI for Urban Growth Prediction Modelling

The fast-evolving nature of urbanization and its complex patterns require precise and interpretable machine learning models to effectively predict urban growth. To address this challenge, this study introduces a novel framework combining Hybrid Intelligence and Explainable AI (XAI), specifically Shapley Additive Explanations (SHAP) to improve model performance, robustness, and transparency. Using a weighted ensemble technique, the proposed method systemically integrates linear, tree-based, and neural network models to propose a hybrid of Elastic Net, XGBoost, and Wide & Deep Neural Network (EN-XGB-WDN) frameworks for urban growth prediction. The methodology follows a multistep approach and includes the development of the hybrid model, its evaluation for binary classification, integration of SHAP-based feature analysis to identify key drivers of urban growth and improve model interpretability, retraining of the hybrid model to increase accuracy and reduce overfitting, and validation of the proposed framework using standard evaluation metrics including accuracy, precision, recall, F1 score, and AUC. The hybrid model achieves an overall accuracy of 87.34%, a weighted F1-score of 87.18%, and an AUC of 0.9442. The SHAP analysis revealed that Drive Time (DT), Distance from Roads (DfR), and Elevation are the most impactful features to understand the dynamics of urban growth. The findings revealed how variations in specific features, such as higher DT and lower DfR, significantly affect urban growth probabilities. The hybrid model also categorized urban growth probabilities into five classes: very low (40.62%), low (23.27%), moderate (15.38%), high (12.10%), and very high (8.63%), revealing spatial patterns of urban expansion. The framework combines hybrid ensemble methods with SHAP-based explanations to significantly enhance the predictive and explanatory power of urban growth models compared to the limitations of traditional approaches. This study highlights the efficiency of integrating hybrid machine learning and Explainable AI to understand and predict complex urbanization dynamics. The outcomes offer actionable insights for policymakers and urban planners, facilitating data-driven strategies for sustainable urban development. This research demonstrates the effectiveness of hybrid intelligence coupled with Explainable AI, offering a scalable and interpretable framework to better understand and predict urbanization patterns.