Machine Learning Approach to Identify Promising Mountain Hiking Destinations Using GIS and Remote Sensing.

Naimi, Lahbib; Ouaddi, Charaf; Benaddi, Lamya; Bouziane, El Mahi; Jakimi, Abdeslam; Manaouch, Mohamed

The objective of this study is to address the complex task of identifying optimal locations for mountain hiking sites in the Eastern High Atlas region of Morocco, considering topographical factors. The study assesses the effectiveness of a commonly used machine learning classifier (MLC) in mapping potential mountain hiking areas, which is crucial for promoting and enhancing tourism in the area. To begin with, an extensive inventory of 120 mountain hiking sites was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. The model achieved a high performance, with an area under the curve (AUC) value of 0.93. The model effectively identified favorable areas, encompassing around 24% of the study region, which were predominantly located in the western part. These areas were characterized by mountainous terrain, shorter slopes, and higher altitudes. The research findings provide valuable guidance to decision-makers, offering a roadmap to enhance the discovery of mountain hiking sites in the region.

MACHINE learning; GEOGRAPHIC information systems; REMOTE sensing; HIKING; TOURISM

International Journal of Advanced Computer Science & Applications, 2024, Vol 15, Issue 10, p980

2158-107X

Academic Journal

10.14569/ijacsa.2024.0151099