## 2 Literature Review
[PERSON] et al. (2013) used big data technology to quickly grab and intelligently analyze massive information to provide decision support for natural disasters [16]. [PERSON] et al. (2021) elaborated that in the complex processing process of big data, artificial intelligence, cloud computing, deep learning, machine learning, and other digital technologies are needed to support [17]. [PERSON] et al. (2022) stated that in the emergency response and disposal stage, artificial intelligence technology can realize automatic disaster response reporting through geographic information data and social media network data fused with deep-learning technology and realize an emergencyevacuation route map through satellite image fusion image-recognition technology and machine-learning technology [18]. Therefore, algorithms and artificial intelligence technologies will be employed in the development of rural emergency management models. [PERSON] et al. (2021) utilized blockchain technology for monitoring and early warning in an intelligent emergency system. Upon activation of the early warning contract, real-time sharing of original data and epidemic synchronous detection can be achieved through network broadcasting, transforming the traditional hierarchical early warning arrangement into a more flexible geared approach. This enables organizations at all levels to promptly access early warning information simultaneously and respond instantaneously [19]. Furthermore, [PERSON]'s research (2022) also demonstrates that information and communication technology can enhance public awareness and mitigate the adverse impacts of natural disasters [20]. In terms of emergency management, [PERSON] (2023) asserts that significant attention has been given to frontier technologies such as 5G, artificial intelligence, big data, and blockchain within government planning and policy frameworks [21]. [PERSON] (2022) analyzes the strategic role of new-generation information technology in emergency management [22]. [PERSON] (2019) illustrates how integrating the Internet of Things with big data facilitates disaster emergency response systems for smart cities [23]. [PERSON] et al. (2020) proposed establishing a comprehensive earthquake emergency response framework that proactively responds by uniting rescue forces from various sources under unified command to achieve interconnection and the sharing of disaster-related information [24]. [PERSON] et al. 's proposal (2020) involves employing autonomous unmanned aerial vehicles (UAVs) for forest fire emergency rescue operations [25]. [PERSON] (2016) conducted a study on the challenges faced by local governments at the grassroots level in emergency management, emphasizing the significance of focusing on the pivotal role played by social media in emergency response and recovery. Additionally, an evaluation framework was developed [26]. [PERSON] (2023) proposed a novel framework that utilizes classification algorithms to process perception data, relies on the Internet of Things, and integrates multiple machine-learning models for the real-time collection of severe weather data that may lead to floods. This framework also sends early alerts to users and rescue teams in case of any potential threats [27]. [PERSON] et al. (2011) suggested a theoretical approach for constructing disaster emergency management capacity by combining data collection and analysis with real-time decision-making processes. They further recommended using mobile phone network data to track population flow during emergencies, enabling prompt rescue operations and timely treatment for affected individuals [28]. [PERSON] et al. (2017) examined necessary changes required for future emergency management implementation in the United States. They identified challenges and opportunities across five areas: climate change, leadership, funding, communications and emerging technologies, and partnerships, as well as cultural promotion [29]. [PERSON] et al.