Research on an evaluation index system of critical emergency management capability based on machine learning in a complex scientific environment - Zhao, Xianli; Hu, Zhenlong (Frontiers in Ecology and Evolution, 2023)
A complex scientific environment requires multiple considerations for handling critical and emergency conditions with an addressing solution. Indexing and prioritizing are standard in such settings for improving itinerary solutions. The significance of an indexing system relies on the benchmark solution and strategy it implies. This article introduces an Indexing Strategy Evaluation Method (ISEM) for validating the efficiency of indexing systems. The proposed method identifies the root implication and the strategy parameters for addressing complex problems. The environmental and problem-specific parameters are determined to estimate the system’s initial response. The capability through solution response, lag, and failures is identified post the estimation through linear regression learning. The indexing system operations are designed through linear itineraries to prevent interrupting failures. Besides, the environmental features are identified as augmenting factors for preventing strategy pausing across multiple indices. The proposed method employs linear analysis through itinerary levels of index evaluation for optimal, lagging, and failed implications. This helps to identify specific reasons for solution improvement or retention from the previous operations.