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Climate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in order to increase that capability is of great practical importance. The evaluation of meteorological disaster emergency response capability still has some issues. The majority of research methods use qualitative analysis, which makes it challenging to deal with fuzzy factors, leading to conclusions that are subjective and insufficiently rigorous. The evaluation models themselves are also complex and challenging to simulate and analyze, making it challenging to promote and use them in practice. Deep learning techniques have made it easier to collect and process large amounts of data, which has opened new avenues for advancement in the emergency management of weather-related disasters. In this paper, we suggest a Recurrent Neural Network (RNN)-based dynamic capability feature extraction method. The process of evaluation content determination and index selection is used to build a meteorological disaster emergency response capability evaluation index system before an encoder, based on the encoder–decoder architecture, is built for dynamic feature extraction. The RNN autoencoder deep learning ability dynamic rating method used in this paper has been shown through a series of experiments to be able to not only efficiently extract ability features from time series data and reduce the dimensionality of ability features, but also to reduce the focus of the ability evaluation model on simple and abnormal samples, concentrate the model learning on difficult samples, and have a higher accuracy. As a result, it is more suitable for the problem situation at evaluation of the disaster capability. [ABSTRACT FROM AUTHOR]

In 2022, as a result of the historically exceptional high temperatures that have been observed this summer in several parts of China, particularly in the province of Sichuan, residential demand for energy has increased. Up to 70% of Sichuan's electricity comes from hydropower, thus creating a sensible and practical reservoir scheduling plan is essential to maximizing reservoir power generating efficiency. However, classical optimization, such as back propagation (BP) neural network, does not take into account the correlation of samples in time while generating reservoir scheduling rules. We proposed a prediction model based on LSTM neural network coupled with wavelet transformation (WT-LSTM) to address the problem. In order to extract the reservoir scheduling rules, this paper first gathers the scheduling operation data from the Xiluodu hydropower station and creates a dataset. Next, it uses the feature of the time-series prediction model with the realization of a complex nonlinear mapping function, time-series learning capability, and high prediction accuracy. The results demonstrate that the time-series deep learning network has high learning capability for reservoir scheduling by comparing evaluation indexes such as root mean square error (RMSE), rank-sum ratio (RSR), and Nash–Sutcliffe efficiency (NSE). The WT-LSTM network model put forward in this research offers better prediction accuracy than conventional recurrent neural networks and serves as a reference base for scheduling decisions by learning previous scheduling data to produce outflow solutions, which has some theoretical and practical benefits. [ABSTRACT FROM AUTHOR]