Among the various operations performed on fuzzy spatiotemporal data, determining topological relations is particularly significant. To illustrate this, consider the challenge of monitoring weather conditions in Hong Kong. The inquiry regarding the presence of rainfall in Hong Kong possesses a spatiotemporal dimension, necessitating an evaluation of cloud cover over the region across a sequence of events and time intervals. This involves analyzing various scenarios, such as periods when the cloud and Hong Kong are disjoint, instances when they intersect at specific time points, durations of overlap, situations where the cloud is contained within Hong Kong, and eventual returns to disjoint states. However, relying solely on topological relations between the cloud and Hong Kong may lead to erroneous conclusions regarding rainfall. For instance, the presence of clouds does not necessarily indicate rain, particularly if the cloud cover is minimal. A cloud cover of 2% may suggest clear conditions, while a 30% cloud cover could indicate overcast skies without precipitation. Therefore, to enhance the precision of determining topological relations within fuzzy spatiotemporal data, the integration of fuzzy logic into the assessment of cloud cover is proposed.