Tourism Potential Zone Mapping Using MCDM and Machine Learning Models in The State of Madhya Pradesh India

The rich and diverse tourism attractions of Madhya Pradesh have long been recognized, but the Tourism Potential Zones (TPZs) have yet to be clearly identified. This research aimed to uncover these hidden potentials using a combination of Multi-Criteria Decision Making (MCDM) and machine learning techniques. TPZ was predicted using a approaches, including Analytic Hierarchy Process (AHP), Linear Model (LM), Elastic Net Model (EN), and K-Nearest Neighbors (KNN). Further, by combining the above models, a new ensemble model (AHP-LN-EN-KNN ensemble) was prepared. We followed the ROC-AUC (Area Under Curve) and Root Mean Squared Error (RMSE) as evaluation measures. The findings reveal a landscape of promise, with each model with accuracy levels ranging from 81.4% to 90.6%. The AUC values for the models ranged from approximately 70% to 95%, while the RMSE values ranged from 0.8 to 1.3. The ensemble model appeared with better accuracy (for training set 0.92 and for test set 0.88), higher AUC value (for training set 94.5% and for test set 89.4%) and the lowest RMSE (i.e., 0.71) value. On the other hand, the AHP was identified with higher combined RMSE (i.e., combined RMSE 1.08) and diminished AUC (i.e., for training set 70.1% and test set 70.2%). The northern, south-western, and middle regions emerge as high-potential areas, whilst the south-western edges languish with less promise. Meanwhile, the north-western expanse offers a scene of moderate potential. These findings not only inform, inspire, laying a foundation for Madhya Pradesh's long-term tourist growth.