## 1 Introduction
Scholars have delved into government decision-making and implementation mechanisms, striving to ensure the scientific soundness of policies addressing natural disasters. Over time, the theories around natural disaster management have matured and diversified in research directions, a trend echoed in the works of [PERSON] (2023), who emphasized the importance of early disaster recovery strategies in post-disaster implementation in Indonesia [9]. Legal Frameworks and Policies: The study of the formulation of laws, establishment of management systems, and preparation of emergency plans for natural disaster management has been the focal point of many academics, aimed at bolstering governmental emergency management capabilities. The U.S., starting in the 1980s, progressively introduced legislative frameworks such as the Flood Control Act and the Natural Disaster Mitigation Act to govern the emergency management of natural catastrophes [10]. The Disaster Relief and Emergency Assistance Act of 1974 established a foundation for the U.S. federal government to support state and local governments, delineating guidelines for post-disaster relief and rescue operations. Meanwhile, Japan's Disaster Relief Act integrated the concepts of disaster mitigation and prevention into a singular legislative framework. This was succeeded by the Basic Law for Disaster Countermeasures, transitioning Japan's legal framework from a singular response system to a multi-faceted comprehensive system. This shift emphasized institutional structuring, early disaster warnings, plan development, emergency response, and post-disaster recovery and rebuilding [11]. Furthermore, the Basic Countermeasures for Natural Disasters Act laid the foundation for Japan's preventative and mitigation strategies [12]. Innovative Studies and Technological Advancements: Recent studies have ventured deeper into the nuances of management strategies and institutional evolution. [PERSON] et al. (2023) utilized network analysis and the Petri net model to foster organizational collaboration, concentrating on the synergy of emergency management entities and disaster response protocols, thereby enhancing efficiency and reducing response time [13]. On another front, [PERSON] et al. (2023) leveraged a social networking perspective to amplify emergency management competencies, advocating for a collaborative governance mechanism involving multiple entities, particularly emphasizing grassroots governments [14]. In the era of big data, [PERSON] and [PERSON] (2022) navigated the complexities of governmental emergency responses, pinpointing the transformative role of digital government in reshaping government management and business structures and suggesting institutional reforms to harness new opportunities in democratic politics [15]. Similarly, [PERSON] et al. (2020) critically evaluated Taiwan's emergency response mechanisms during the COVID-19 pandemic, proposing avenues for improvement using big data analytics and new technology for proactive testing, thereby safeguarding public health through timely and transparent information dissemination [16]. The recent advancements in information technology have facilitated the standardization and automation of primary behavioral data for meteorological disaster agencies [17]. Consequently, capability evaluations have increasingly incorporated data-driven methodologies, such as machine learning and deep learning, yielding promising results. Nonetheless, integrating deep learning techniques into the assessment of emergency response capacities for weather-related disasters has unveiled certain mismatches and issues [18]. This arises primarily because machine and deep learning algorithms adjust model parameters based on the loss function values of training data, making the loss function a foundational element for model training regarding capacity evaluation rules. The existing loss functions, however, have shown some limitations in assessing weather hazard response capabilities:  
1.