Tsunamis can cause high numbers of casualties, as well as direct and indirect economic losses to coastal regions. The huge destructiveness of tsunamis requires us to study tsunami risk and its temporal change. We adopt the tsunami scenarios of the Ryukyu Trench as an example to analyze the temporal change in tsunami risk. According to the tsunami numerical model results, the tsunami inundation in the worst tsunami scenario covered an area of 82.83 km². Satellite data including Landsat 8 images from July 2013 and Landsat 9 images from March 2022 were used with the random forest (RF) method to analyze land use and tsunami vulnerability in 2013 and 2022. The tsunami risk and its temporal change were analyzed by integrating tsunami hazard and tsunami vulnerability. The tsunami risk change analysis results show that the area of tsunami risk level 1 increased by 4.57 km², and the area of tsunami risk level 4 decreased by 7.31 km². By analyzing changes in land use and land cover (LULC) and tsunami risk, we concluded that the expansion of constructed land and the increase in coastal population were responsible for the increase in tsunami risk. The results of tsunami risk change analysis will help us understand the current tsunami risk and predict possible future risk change. In addition, it is necessary to prepare tsunami prevention measures in advance and produce tsunami emergency response plans for Qidong County and other regions under potential tsunami threat.

Major tsunami disasters often cause great damage in the first few hours following an earthquake. The possible severity of such events requires preparations to prevent tsunami disasters or mitigate them. This paper is an attempt to develop a decision support system for rapid tsunami evacuation for local decision makers. Based on the numerical results database of tsunami disasters, this system can quickly obtain the tsunami inundation and travel time. Because numerical models are calculated in advance, this system can reduce decision-making time. Population distribution, as a vulnerability factor, was analyzed to identify areas of high risk for tsunami disasters. Combined with spatial data, this system can comprehensively analyze the dynamic and static evacuation process and identify problems that negatively impact evacuation, thus supporting the decision-making for tsunami evacuation in high-risk areas. When an earthquake and tsunami occur, this system can rapidly obtain the tsunami inundation and travel time and provide information to assist with tsunami evacuation operations.