Exploring Apulia’s Regional Tourism Attractiveness through the Lens of Sustainability: A Machine Learning Approach and Counterfactual Explainability Process

Fabio Castellana, Roberta Zupo, Filomena Corbo, Pasquale Crupi, Feliciana Catino, Angelo Michele Petrosillo, Orazio Valerio Giannico, Rodolfo Sardone and Maria Lisa Clodoveo ()
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Fabio Castellana: Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70100 Bari, Italy
Roberta Zupo: Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70100 Bari, Italy
Filomena Corbo: Department of Pharmacy-Drug Sciences, University of Bari “Aldo Moro”, 70125 Bari, Italy
Pasquale Crupi: Department of Agricultural, Food and Forest Science, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Feliciana Catino: Unit of Innovation and Smart City, Local Health Authority of Taranto, 74121 Taranto, Italy
Angelo Michele Petrosillo: Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70100 Bari, Italy
Orazio Valerio Giannico: Unit of Statistics and Epidemiology, Local Health Authority of Taranto, 74121 Taranto, Italy
Rodolfo Sardone: Unit of Statistics and Epidemiology, Local Health Authority of Taranto, 74121 Taranto, Italy
Maria Lisa Clodoveo: Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70100 Bari, Italy

Sustainability, 2024, vol. 16, issue 15, 1-13

Abstract: Visitor attraction dynamics lead tourism industry paths. A complex artificial neural network model was built to predict the incoming tourism flow in the Apulia region of Southern Italy as a function of the heterogeneity of the tourism supply available in this area. Open data from the Regional Tourism Observatory were targeted. Information on the distribution of facilities and activities that attract regional tourist flows was collected and grouped by municipality. An artificial neural network model was built with total tourist attendance as the dependent variable and tourist attractions as regressors. The Root Mean Square Error (RMSE) was used to select the optimal model using the lowest value. The final model was run with a hidden layer consisting of three neurons and a decay value of 0.01. A Multi-Objective Counterfactual model (MOC) was then constructed using a randomly selected row of normalized data frame to validate a useful tool in increasing total tourist attendance by 20% over that of the randomly selected municipality. A Garson’s variables importance plot indicated natural landscapes such as beaches, sea caves, and natural parks have a primary role expressed in terms of variable importance in the AI algorithm when used as an innovative methodology for evaluating tourism flows in the Apulia region. A further MOC model built using a randomly selected row of normalized data frame showed convents, libraries, historical buildings, public gardens, and museums as the top five features most modified to improve total attendance in a randomly selected municipality. Use of AI modeling revealed that the implementation of nature-based solutions may speed up the flow of tourism in the Apulia region while also promoting sustainable social development.

Keywords: sustainable climate action; global health; machine learning; neural network analysis; counterfactual analysis; tourism; Apulia; Italy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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