Title: ENHANCING TOURIST EXPERIENCE: A FUZZY TOPSIS APPROACH TO DESTINATION RECOMMENDATION SYSTEMS
<br>Cover Date: 2025-01-01
<br>Cover Display Date: 2025
<br>DOI: 10.30892/gtg.58120-1405
<br>Description: To promote tourism effectively, it is essential to drive flexibility and sustainable growth within the sector. This requires a multi-faceted approach involving various organizations, as well as the development of systems that assist tourists in decision-making. The objective of this research is twofold: (1) to develop a recommendation system based on Fuzzy TOPSIS that integrates data from expert opinions, tourist behavior, and survey feedback, and (2) to design a system that enables tourists to make personalized destination choices through a recommendation process that accommodates diverse user preferences. This research employs a Multi-Criteria Decision Analysis (MCDA) approach using Fuzzy TOPSIS, leveraging survey data collected from 250 respondents, including tourism experts and visitors in Maha Sarakham province. The survey evaluated ten key criteria influencing destination selection, such as accessibility, safety services, and the availability of cafés and coffee shops. Accessibility and safety services emerged as the most critical factors, both receiving the highest scores of 9, while dining options also scored highly with a rating of 7. Conversely, criteria such as shuttle services and proximity to po lice stations were deemed less significant. According to the Fuzzy TOPSIS analysis ranked Phra That Na Dun, Wat Puttha Wanaram, and the Phra Yuen Mongkhon Buddha Image as the top three attractions, showcasing their strong alignment with tourist preferences. Lower-ranked sites, such as Ban Chiang Hian Museum and Chi Long Forest Park, highlight opportunities for development through infrastructural improvements and enhanced marketing efforts. The system’s user satisfaction evaluation demonstrated favorable results, with high ratings for decision-making capability (4.67) and ease of use (4.67), reflecting the system’s ability to align recommendations with user preferences effectively. Quantitative evaluations of the system yielded a precision of 85%, recall of 80%, and an F1-score of 82.42%, indicating a balanced performance in delivering accurate and relevant recommendations. Furthermore, the integration of Fuzzy MCDA with user-centered design ensures that the recommendation system remains adaptable to evolving tourist preferences. This framework demonstrates the importance of integrating flexible, user-centered recommendations into tourism to meet evolving visitor needs effectively.
<br>Citations: 1
<br>Aggregation Type: Journal
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<br><br><br>Title: Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model
<br>Cover Date: 2024-09-01
<br>Cover Display Date: September 2024
<br>DOI: 10.3390/f15091535
<br>Description: Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R<sup>2</sup>) value (FCR) of 0.787, and also the highest R<sup>2</sup> (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.
<br>Citations: 3
<br>Aggregation Type: Journal
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<br><br><br>Title: Spatial Predictive Modeling of Liver Fluke Opisthorchis viverrine (OV) Infection under the Mathematical Models in Hexagonal Symmetrical Shapes Using Machine Learning-Based Forest Classification Regression
<br>Cover Date: 2024-08-01
<br>Cover Display Date: August 2024
<br>DOI: 10.3390/sym16081067
<br>Description: Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.
<br>Citations: 4
<br>Aggregation Type: Journal
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<br><br><br>Title: Spatial prediction of the probability of liver fluke infection in water resource within sub-basin using an optimized geographically-weighted regression model
<br>Cover Date: 2024-01-01
<br>Cover Display Date: 2024
<br>DOI: 10.3389/fvets.2024.1487222
<br>Description: Introduction: Infection with liver flukes (Opisthorchis viverrini) is partly attributed to their ability to thrive in sub-basin habitats, causing the intermediate host to remain within the watershed system throughout the year. It is crucial to conduct spatial monitoring of fluke infection at a small basin analysis scale as it helps in studying the spatial factors influencing these infections. The number of infected individuals was obtained from local authorities, converted into a percentage, and visually represented as raster data through a heat map. This approach generates continuous data with dependent variables. Methods: The independent set comprises nine variables, including both vector and raster data, that establish a connection between the location of an infected person and their village. Design spatial units optimized for geo-weighted modeling by utilizing a clustering and overlay approach, thereby facilitating the optimal prediction of alternative models for infection. Results and discussion: The Model-3 demonstrated the strongest correlation between the variables X5 (stream) and X7 (ndmi), which are associated with the percentage of infected individuals. The statistical analysis showed t-statistics values of −2.045 and 0.784, with corresponding p-values of 0.016 and 0.085. The RMSE was determined to be 2.571%, and the AUC was 0.659, providing support for these findings. Several alternative models were tested, and a generalized mathematical model was developed to incorporate the independent variables. This new model improved the accuracy of the GWR model by 5.75% and increased the R<sup>2</sup> value from 0.754 to 0.800. Additionally, spatial autocorrelation confirmed the difference in predictions between the modeled and actual infection values. This study demonstrates that when using GWR to create spatial models at the sub-basin level, it is possible to identify variables that are associated with liver fluke infection.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: Built-up growth impacts on digital elevation model and flood risk susceptibility prediction in Muaeng District, Nakhon Ratchasima (Thailand)
<br>Cover Date: 2019-07-01
<br>Cover Display Date: 1 July 2019
<br>DOI: 10.3390/w11071496
<br>Description: The transformation of land-use and land cover in Nakhon Ratchasima province, Thailand has rapidly changed over the last few years. The major factors affecting the growth in the province arise from the huge expansion of developing areas, according to the government's development plans that aim to promote the province as a central business-hub in the region. This development expansion has eventually intruded upon and interfered with sub-basin areas, which has led to environmental problems in the region. The scope of this study comprises three objectives, i.e., (i) to optimize the Cellular Automata (CA) model for predicting the expansion of built-up sites by 2022; (ii) to model a linear regression method for deriving the transition of the digital elevation model (DEM); and (iii) to apply Geographic Weighted Regression (GWR) for analyzing the risk of the stativity of flood areas in the province. The results of this study show that the optimized CA demonstrates accurate prediction of the expansion of built-up areas in 2022 using Land use (LU) data of 2-year intervals. In addition, the predicting model is generalized and converged at the iteration no. 4. The prediction outcomes, including spatial locations and ground-water touch points of the construction, are used to estimate and model the DEM to extract independent hydrology variables that are used in the determination of Flood Risk Susceptibility (FRS). In GWR in the research called FRS-GWR, this integration of quantitative GIS and the spatial model is anticipated to produce promising results in predicting the growth and expansion of built-up areas and land-use change that lead to an effective analysis of the impacts on spatial change in water sub-basin areas. This research may be beneficial in the process of urban planning with respect to the study of environmental impacts. In addition, it can indicate and impose important directions for development plans in cities to avoid and minimize flood area problems.
<br>Citations: 13
<br>Aggregation Type: Journal
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