LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO
The present study aims to tackle the complex task of identifying optimal areas for defining geomorphosites in large regions, considering various influencing factors. The study focuses on Ziz Upper Watershed (ZUW), southeast Morocco, and evaluates the effectiveness of the commonly used machine lear...
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Editura Universităţii din Oradea
2024-12-01
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| Series: | Geo Journal of Tourism and Geosites |
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| Online Access: | https://gtg.webhost.uoradea.ro/PDF/GTG-4spl-2024/gtg.574spl18-1371.pdf |
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| author | Lahbib NAIMI Mohamed MANAOUCH Abdeslam JAKIMI |
| author_facet | Lahbib NAIMI Mohamed MANAOUCH Abdeslam JAKIMI |
| author_sort | Lahbib NAIMI |
| collection | DOAJ |
| description | The present study aims to tackle the complex task of identifying optimal areas for defining geomorphosites in large
regions, considering various influencing factors. The study focuses on Ziz Upper Watershed (ZUW), southeast Morocco, and
evaluates the effectiveness of the commonly used machine learning classifier (MLC) in mapping potential geomorph osite
areas. The identification and mapping of such areas are crucial for attracting and enhancing geotourism in the region. Initia lly,
a comprehensive inventory of 120 geomorphosites was conducted, and precise measurements of three topographical
parameters were taken at each site. Subsequently, the machine learning algorithm, namely Bagging was employed to develop
predictive model. The performance, achieving an area under the curve (AUC) of 0.935. This models successfully identified
highly favorable areas, encompassing approximately 12% of the study area. These favorable areas were predominantly
situated in the western region of the study area, characterized by mountainous terrain with relatively shorter slope lengths and
high altitudes. The findings of this research provide valuable guidance to decision-makers, offering a roadmap for improving
the chances of discovering geomorphosites. |
| format | Article |
| id | doaj-art-fdc0750cc6854b2cbd5d3194c4550e3f |
| institution | DOAJ |
| issn | 2065-0817 2065-1198 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Editura Universităţii din Oradea |
| record_format | Article |
| series | Geo Journal of Tourism and Geosites |
| spelling | doaj-art-fdc0750cc6854b2cbd5d3194c4550e3f2025-08-20T02:42:00ZengEditura Universităţii din OradeaGeo Journal of Tourism and Geosites2065-08172065-11982024-12-01574 supplement2041205110.30892/gtg.574spl18-1371LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCOLahbib NAIMI0Mohamed MANAOUCH1Abdeslam JAKIMI2Moulay Ismail University, GL-ISI Team, Department of Computer Science, Faculty of Science and Technology, Errachidia, Morocco, e-mail: l.naimi@edu.umi.ac.maIbn Tofail University, Department of Geography, Faculty of Humanities and Social Sciences, Kenitra, Morocco, e-mail: mohamed.manaouch@uit.ac.maMoulay Ismail University, GL-ISI Team, Department of Computer Science, Faculty of Science and Technology, Errachidia, Morocco, e-mail: ajakimi@yahoo.frThe present study aims to tackle the complex task of identifying optimal areas for defining geomorphosites in large regions, considering various influencing factors. The study focuses on Ziz Upper Watershed (ZUW), southeast Morocco, and evaluates the effectiveness of the commonly used machine learning classifier (MLC) in mapping potential geomorph osite areas. The identification and mapping of such areas are crucial for attracting and enhancing geotourism in the region. Initia lly, a comprehensive inventory of 120 geomorphosites was conducted, and precise measurements of three topographical parameters were taken at each site. Subsequently, the machine learning algorithm, namely Bagging was employed to develop predictive model. The performance, achieving an area under the curve (AUC) of 0.935. This models successfully identified highly favorable areas, encompassing approximately 12% of the study area. These favorable areas were predominantly situated in the western region of the study area, characterized by mountainous terrain with relatively shorter slope lengths and high altitudes. The findings of this research provide valuable guidance to decision-makers, offering a roadmap for improving the chances of discovering geomorphosites.https://gtg.webhost.uoradea.ro/PDF/GTG-4spl-2024/gtg.574spl18-1371.pdfgeositesgeotourismgeomorphositegeoparkmachine learningse morocco |
| spellingShingle | Lahbib NAIMI Mohamed MANAOUCH Abdeslam JAKIMI LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO Geo Journal of Tourism and Geosites geosites geotourism geomorphosite geopark machine learning se morocco |
| title | LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO |
| title_full | LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO |
| title_fullStr | LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO |
| title_full_unstemmed | LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO |
| title_short | LEVERAGING MACHINE LEARNING ALGORITHMS TO IDENTIFY POTENTIAL GEOSITES FOR GEOTOURISM PROMOTION IN ZIZ UPPER WATERSHED IN SOUTHEASTERN MOROCCO |
| title_sort | leveraging machine learning algorithms to identify potential geosites for geotourism promotion in ziz upper watershed in southeastern morocco |
| topic | geosites geotourism geomorphosite geopark machine learning se morocco |
| url | https://gtg.webhost.uoradea.ro/PDF/GTG-4spl-2024/gtg.574spl18-1371.pdf |
| work_keys_str_mv | AT lahbibnaimi leveragingmachinelearningalgorithmstoidentifypotentialgeositesforgeotourismpromotioninzizupperwatershedinsoutheasternmorocco AT mohamedmanaouch leveragingmachinelearningalgorithmstoidentifypotentialgeositesforgeotourismpromotioninzizupperwatershedinsoutheasternmorocco AT abdeslamjakimi leveragingmachinelearningalgorithmstoidentifypotentialgeositesforgeotourismpromotioninzizupperwatershedinsoutheasternmorocco |