DOI: https://doi.org/10.14710/geoplanning.12.1.%25p

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

*Shrinwantu Raha orcid  -  Department of Geography, Bhairab Ganguly College, Belgharia, Kolkata, Pin Code:7,000,56, India
Sayan Deb orcid  -  Department of Geography, Bhairab Ganguly College, Belgharia, Pin Code: 7,000,56, India

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Abstract

The rich kaleidoscope of Madhya Pradesh's tourism attractions has long been acknowledged, but the delineation of Tourism Potential Zones (TPZs) remain enigmatic. This work aims to uncover these hidden jewels using a combination of Multi-Criteria Decision Making (MCDM) and machine learning techniques. TPZ was investigated using a variety of approaches, including Analytic Hierarchy Process (AHP), Linear Model (LM), Elastic Net Model (EN), and K-Nearest Neighbour (KNN). Further, by combining each the above models, a new ensemble model (AHP-LN-EN-KNN ensemble) was prepared. We followed the ROC-AUC curve and Root Mean Squared Error (RMSE) as evaluation measures. The findings reveal a landscape of promise, with each model shining with accuracy levels ranging from 81.4% to 90.6%. AUC scores ranged from 70% to 93%, with RMSE values ranging from 0.8 to 1.3. The ensemble model was embarked with a better accuracy (for training set 0.92 and for test set 0.88), AUC value (for training set 94.2% and for test set 87.2%) and lowest RMSE (i.e., 0.71), while AHP languishes at the rear, burdened by its elevated RMSE and diminished AUC. 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, but also inspire, laying a foundation for Madhya Pradesh's long-term tourist growth. They encourage stakeholders to maintain and grow these designated zones, building a future in which the state's tourism thrives in tandem with its natural and cultural assets.

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Keywords: Tourism Potential Zone (TPZ); K-Nearest Neighbour model; Analytic Hierarchy Process; Elastic Net Model; Linear Model.
Funding: NA

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Section: Original Research
Language : EN
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