| dc.description.abstract | In recent years, due to the impacts of climate change and Taiwan′s natural topography of high mountains and swift rivers, rainfall has become highly uneven in time and space. The dense population, along with the unfavorable development of agriculture, industry, and economy, has led to inadequate development of water resources and reduced groundwater recharge. Moreover, changes in the spatial and temporal rainfall characteristics have given rise to various hydrological and environmental problems, including extreme events. Taiwan′s main rainy seasons occur during the Meiyu season from May to June and the typhoon season from July to September. There is a significant difference in rainfall between the wet and dry seasons, but Taiwan′s water demand primarily relies on the rainfall brought by the Meiyu and typhoons. Therefore, even slight changes in rainfall patterns can directly or indirectly affect Taiwan′s risk of droughts and floods. Thus, understanding the spatiotemporal variability of rainfall in different regions and the natural factors influencing its variation, as well as predicting them, has become a critical issue of concern.
This study focuses in Taiwan , using data mining techniques to analyze observed data and remote sensing data at macro spatial and temporal scales. Wavelet signal analysis is used to extract features and explore the correlations between rainfall and factors that may affect hydrological and water resource variability. The study found that sunspots exhibit a significant 10- to 12-year cycle, with the number of sunspots varying approximately every 11 years. The Southern Oscillation Index (SOI) has a more significant correlation with a cycle of 2 to 8 years, which is closely related to the El Niño-Southern Oscillation (ENSO) phenomenon. In addition, the wavelet coherence analysis between SSN, SOI, and Taiwan′s rainfall shows a high correlation not only in the frequency range of 10 to 12 years but also in the range of 2 to 8 years. Furthermore, the wavelet coherence coefficient between sunspots (SSN) and Taiwan′s rainfall has been gradually increasing since 1990.
Moreover, this study employs three machine learning models to predict and classify rainfall levels, with the Naive Bayes classifier achieving the highest accuracy rate of up to 89.9%. It is found that the sunspot number has the highest contribution to the prediction of rainfall levels, accounting for 34% of the total contribution. Additionally, this study improves upon the limitations of other black-box machine learning models, such as neural networks, by using a Bayesian network model to analyze the causal relationships between factors that may affect hydrological resources and rainfall variation characteristics in Taiwan. The conditional probability is used to quantify the degree of influence between these factors. This research hopes to provide a better understanding of the rainfall mechanism in Taiwan and to serve as a reference for Taiwan′s water resource management policies. | en_US |