Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms

Abstract

Super Typhoon Rai damaged Siargao’s tourism industry. Despite the reconstruction projects, there was still evidence of limited resources, destructed infrastructures, and destroyed natural resources. Therefore, this study aimed to examine the significant factors influencing tourists’ intentions to revisit Siargao after Super Typhoon Rai using feature selection, logistic regression (LR), and an artificial neural network (ANN). It employed three feature-selection techniques, namely, the filter method’s permutation importance (PI), the wrapper method’s Recursive Feature Elimination (RFE), and the embedded method’s Least Absolute Shrinkage and Selection Operator (LASSO). Each feature-selection technique was integrated into LR and the ANN. LASSO-ANN, with a 97.8146% model accuracy, was found to be the best machine-learning algorithm. The LASSO model performed at its best with a 0.0007 LASSO alpha value, resulting in 35 subfeatures and 8 primary features. LASSO subsets underwent the ANN model procedure, and the optimal parameter combination was 70% training size, 30% testing size, 30 hidden-layer nodes, tanh hidden-layer activation, sigmoid output-layer activation, and Adam optimization. All eight features were found to be significant. Among them, hedonic motivation and awareness of Typhoon Rai’s impact were considered the top-tier post-typhoon tourism factors, as they maintained at least 97% prediction accuracy. The findings could be elaborated by combining feature-selection techniques, utilizing demographic characteristics, assessing Siargao’s tourism before the typhoon, and expanding the context and participant selection. Nevertheless, none of the existing studies explored the combination of feature selection, LR, and ANNs in a post-typhoon tourism context. These unique methods and significant findings represent the study’s novelty. Furthermore, practical contributions were provided through economic resolutions focusing on tourism activities and communication revamping by the government, media outlets, and transportation companies.

Exploring Employee Retention among Generation Z Engineers in the Philippines Using Machine Learning Techniques

“…A Likert scale functions as a measurement tool designed to assess respondents' viewpoints, attitudes, or degrees of agreement in response to survey statements. It is the most applicable type of questionnaire in predicting human behavior through the inclusion of machine learning algorithms [6]. Demographic responses were also collected from the participants, including their age, gender, educational attainment, employment status, employment tenure, and average monthly income.…”

“…It rearranges features randomly until a score is assigned to the best subfeature combination. This technique was chosen because it was found the best among all filter methods due to its unpredictability criteria [6].…”

“…The parameters chosen were 70% training size, 30% testing size, and 5 K permutation replicates. The past study displayed adequate accuracy with positive effects on the class while utilizing these parameters [6]. This approach meant that 70% of the data were trained before the final accuracy calculation.…”

“…Feature selection is an advanced machine learning tool that determines important features affecting the class [6]. It comprises three main techniques, which are filter, wrapper, and embedded.…”

“…They are chosen due to their ability to reduce data noise and maximize small datasets [7]. These feature selection techniques are merged with other machine learning algorithms to investigate human perceptions [6], specifically, the factors affecting the retention rate of Generation Z engineers. The combination of feature selection algorithms mitigates the chances of underfitting and overfitting.…”