## 5 Conclusions  
This study analyzes the functional characteristics of the Chongqing metro network, establishes a topological model of the metro network, and investigates its network properties. By constructing a service efficiency function and resilience recovery model, it explores recovery strategies for metro nodes in high-risk areas of landslide and flood disasters when key nodes fail. The main conclusions are shown as follows. (1) Using the K-means algorithm, the study analyzes the impacts of elevation, rainfall, and slope on disaster risk, dividing the research area into five risk levels. The northern districts of Chongqing, such as Yuzhong, Yubei, and Jiangbei, are at higher risk of landslides and floods due to heavy rainfall, complex terrain, and low elevation. Among the high-risk landslide disaster nodes, there are two transfer stations (Daping and Fuhua Road), while most other nodes have a service efficiency index generally below 0.2. Conversely, the high-risk flood disaster nodes are non-transfer stations and are mostly located along metro lines with high traffic flow, demonstrating generally high service efficiency index; some stations, like Bijin Station, even exceed 0.3. (2) When disasters cause the complete failure of key nodes, the network service efficiency significantly declines. In the case of landslide disasters, service efficiency drops to 0.00627, and in the case of flood disasters, it decreases to 0.00975. Compared to the normal state of network service efficiency (0.0393), the efficiency after node failure decreases by 84.0% and 75.2%, respectively. Among different recovery strategies, the resilience optimization recovery strategy based on genetic algorithms outperforms random repair, topology importance-based, and service efficiency importance-based strategies. In the case of key node failures during landslide disasters, the resilience values for the three strategies are 0.440, 0.441, and 0.570, while the resilience value for the optimal recovery strategy is 0.628, representing improvements of 42.9%, 42.4%, and 10.3%, respectively. A similar trend is observed in flood disasters, further confirming the significant enhancement of network resilience by the genetic algorithm optimization strategy in both disaster scenarios. Although this study focuses on exploring the resilience of the metro network of Chongqing, it also provides many new insights for other cities faced with similar challenges. Firstly, the study confirms that when assessing the resilience of transportation networks, it is important to consider both their topological structures and passenger flows. Secondly, the study shows that transportation networks might have very different resilience performance for different disasters, such as landslides and floods. Therefore, it is important to explore the resilience of transportation networks under different disasters separately to develop the corresponding countermeasures, rather than produce some fixed ones. Thirdly, the study demonstrates the superiority of optimal recovery strategies in improving the resilience of transportation networks. Considering that transportation networks vary greatly between cities, it is also essential for them to develop customized optimal recovery strategies based on their transportation features. Nevertheless, this study does have its limitation. Firstly, regarding the impact of landslides and floods on the metro network, it only considers the failure of metro stations. However, both disasters might also strike metro segments, which are expected to be more destructive, as trains usually run faster on segments. Future studies might further explore the resilience of the metro network in the event of metro segment failures. Secondly, recovery strategies do not consider resource constraints. However, the repair time of metro stations after disasters is greatly influenced by the available resources, including workers, devices, costs, etc. In practice, these resources are often limited to increase the repair time.