The tourism industry today is an important source of income, an effective factor in cultural exchanges between countries, and the world's largest service industry. Especially in developed countries, tourism plays a crucial role in combating poverty and increasing income, reducing unemployment, and boosting economic prosperity, thereby improving the quality of life and increasing social welfare. In recent years, rural tourism has emerged as one of the most important and fundamental strategies for the growth and development of rural areas since the 1950s (Liu et al., 2020). The tourism industry has been one of the most important development activities in developing countries, especially in rural areas (Liu et al., 2020). In rural areas, tourism plays an important role in combating poverty and unemployment (Cunha et al., 2020;Liu et al., 2020). In addition to becoming an integral part of rural development strategies (Ayhan et al., 2020;Carneiro et al., 2015), it is also a sustainable driver of rural socio-economic development (Randelli & Martellozzo, 2019). New investments in the tourism industry are bringing sustainable job opportunities with an acceptable level of income (Martinez et al., 2019). Rural areas have different capacities and attractions, which have led to the development of various types of tourism (Xu et al., 2017). However, tourism activities also carry environmental risks, such as resource waste (Martínez et al., 2019), soil and water pollution (Ciarkowska, 2018) and serious damage to ecosystem services .Selecting appropriate locations for tourism activities is a necessary step for maintaining environmental sustainability. Tourism attractions should be used properly to reduce the vulnerability of ecosystems and prevent waste of tourist's time and money. The best location for tourism activities has been assessed using a variety of quantification methodologies, which are summarized in Table 1. In Jeong et al. ( 2016), high-potential suitability areas (17.3% of the region) were determined using the weighted linear combination (WLC) method. However, various approaches have been developed to evaluate tourism sites in recent years, including Geographic Information Systems (GIS), Multi-Criterion Assessments (MCEs), fuzzy logic and network analysis processes (ANPs) (Aliani et al., 2017), and remote sensing techniques (Ayhan et al., 2020). Based on the MCDS method, it is possible to generate land suitability maps with different risk levels in GIS. Fuzzy-Analytic Hierarchy Process (F-AHP) was used by Zabihi et al. (2020) for assessing the relative importance of natural, environmental, and socio-economic factors in Babol for ecotourism. Among these models, the F-AHP model proved mNost reliable for identifying tourist destinations (Mokarram & Sathyamoorthy, 2016).Ordered Weighted Averaging (OWA) is a multicriteria evaluation method developed by Cheng et al. (2012). In contrast to the previous models, it incorporates interactions between contrasting objectives to gather data and make a collective decision. Accor...
The research aims to classify alluvial fans’ morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (Af) and minimum fan height (Hmin-f). The feature selection algorithm identified (Hmin-f), maximum fan height (Hmax-f), minimum fan slope, and fan length (Lf) to be the morphometries most important for determining formation material, and basin area, fan area, (Hmax-f) and compactness coefficient (Cirb) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87).
Alluvial fans of 4 watersheds in Iran were extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amounts of erosion, and formation material were investigated using the self-organizing map (SOM) method. A feature-selection algorithm was used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm was employed to predict erosion and formation material based on morphometrics. The results indicated that the semi-automatic method in GIS was able to detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting the erosion were fan area and minimum fan height. The feature selection algorithm identified minimum fan height, maximum fan height, minimum fan slope, and fan length to be the morphometrics most important for determining formation material, and basin area, fan area, maximum fan height and C irb were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R 2 = 0.94, R 2 = 0.87).
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