## 1 Introduction
The capability spectrum across weather disaster emergency response agencies is diverse, and current loss functions tend to undervalue agencies with lower capabilities. For instance, out of the capability evaluation results for 1249 agencies released by the National Meteorological Administration in 2017, only 121 agencies scored below grade A. This indicates an unbalanced distribution of capability levels. With the current loss function, during the training of deep learning models, a handful of low-grade agencies might be misclassified as high-grade ones. Drawing from 2017 data, even if all 1370 agencies were ranked AA or above, the cumulative loss would merely be 8.8%. 2. Due to factors like inconsistent attention, variance in evaluation difficulty for institutional meteorological disaster response capabilities, and the present loss function, it becomes arduous to focus the evaluation model on entities that pose evaluative challenges. The essence of a capability evaluation is to spotlight agencies that demonstrate subpar capabilities, enabling the deployment of specific, tailored supervision [19]. However, evaluation bodies don't uniformly allocate attention to their assessment outcomes. While the capability evaluation model can more easily discern capability levels from metrics like institution size and qualifications, for a minority of entities, ascertaining their capability levels via competency attributes is tough. Furthermore, agencies, especially those with diminished capabilities or those that are hard to evaluate, draw significant attention from actual management. The standard loss function used in contemporary machine learning models for capability evaluation struggles to adapt to imbalanced data. It disproportionately emphasizes easy-to-identify samples over complex ones, undermining the model's generalization performance [20]. Addressing the challenges of overlooking hard-to-rate agencies and accurately distinguishing low-capacity disaster defense entities in current capacity evaluation models requires a nuanced approach [21]. Enhancing the deep learning algorithm from the perspective of the loss function offers a promising avenue. The modified loss function, during the training of the deep-learning-based capability evaluation model, can be tuned to spotlight disaster defense agencies that are inherently complex to categorize [22]. By doing this, the penalty for inaccurately classifying low-rated and challenging-to-rate agencies is augmented. Here's an in-depth look at the steps proposed:1. Weighted Loss Function Addition: By embedding a weighted loss function, the model is oriented toward paying extra attention to specific low-capacity-rank agencies. Given the uneven capability distribution among weather disaster emergency response agencies, the traditional loss function can be appropriately weighted [23]. By doing so, the loss function value for sparse low-capacity samples is augmented, leading to higher penalties for misclassifications. Consequently, the inherent losses from poor assessments of low-capacity agencies are curtailed [24]. 2. Adaptive Focal Loss Function Integration: To focus on meteorological disaster response agencies with ambiguous capability levels, an adaptive focal loss function is established. This function, crafted around the classification error trends exhibited by agencies during model training, instructs the evaluation model to zero in on those agencies that present significant evaluative challenges. Theoretically, the focal loss function rectifies the imbalance between high and low capability samples throughout training, potentially outperforming preceding methods. The idea is that this scaling factor will automatically decrease the weight of straightforward samples during training, spotlighting those tricky-to-identify low-capacity entities. 3. Comprehensive WAFL Loss Construction: Combining the previously discussed loss functions, a holistic WAFL (Weighted Adaptive Focal Loss) function is proposed.