## 1 Introduction  
As climatic volatility continues to escalate worldwide, the importance of preemptive evaluations in protecting communities from meteorological disasters has become increasingly paramount. These calamities, encompassing hurricanes, typhoons, tornadoes, and floods, not only unleash widespread destruction but also precipitate a harrowing loss of life and crippling economic setbacks. Hence, a meticulous assessment of disaster prevention mechanisms is vital, facilitating the fortification of areas like early warning systems that are crucial in curtailing damages. Through a nuanced appraisal of these mechanisms, authorities can channel resources judiciously, mitigating risks and fostering a state of readiness for potential emergencies. Consequently, the discerning evaluation of meteorological disaster prevention capabilities stands as a cornerstone in minimizing the adverse impacts of extreme weather phenomena on human life, assets, and the environment. Historically, the proficiency of weather disaster response agencies has been gauged predominantly through expert rating methods. Despite their reliability, these methodologies are often marred by substantial time investments and financial burdens, consequently hampering the timeliness and efficacy of evaluations. As we navigate these complex challenges, enhancing the emergency management capabilities of local governments emerges as a pivotal venture. A focus on the rigorous evaluation of government functionalities, encompassing their practices, efficacy, and efficiency, can potentially revolutionize the approach to emergency preparedness. By harnessing a scientifically robust assessment index system, governments stand to refine their operationssignificantly, slashing administrative overheads and amplifying overall effectiveness. This study, therefore, ventures into the realms of deep learning and data science, pioneering an innovative approach to tackle the intrinsic challenges of capability evaluations. Drawing upon the insights from recent studies such as [PERSON] (2023), it proposes adept solutions to counter the imbalances in sample data, setting a new standard for meteorological disaster defense evaluations [1]. Expertise in Emergency Management: [PERSON]. [PERSON] (2009) posited that social organizations have inherent expertise in emergency management. They argued that governmental management agencies should oversee natural disaster emergency responses, emphasizing the need for comprehensive capabilities spanning pre-disaster preparedness to post-disaster recovery [2]. [PERSON] et al. (2006) explored factors influencing emergency response capabilities, encompassing technology, information, cooperative communication, and cultural development [3]. Adding to this discourse, [PERSON] et al. (2023) highlighted the necessity for a coordinated system to manage flood disasters in Malaysia, suggesting a more efficient framework for flood governance [4]. Integration of Performance Monitoring Methods: [PERSON] et al. (2010) suggested incorporating performance monitoring methods from the U.S. education sector into the Government Performance and Results Approach (GPRA) to enable holistic evaluations [5]. Meanwhile, [PERSON] et al. (2012) employed a wavelet neural network in assessing coal mine inundation disaster response capacities [6]. [PERSON] et al. (2017) proposed a model to assess emergency organization capabilities, offering insights for varying local emergency management entities [7]. In a similar vein, [PERSON] et al. (2023) con-ducted a comprehensive survey on the application of machine learning algorithms in emergency management, identifying promising avenues for leveraging these technologies in various phases of disaster management [8]. Academic Interest in Natural Disasters: The academic sphere has long been intrigued by natural disasters, with a recent surge in interest around the theoretical exploration of crisis management due to societal shifts caused by human rights movements, ideological changes, and economic crises.