No abstract

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.

Water pollution control is crucial for ecological environmental safety and sustainable socio-economic development. Public Private Partnership (PPP) collaboration is an important approach for water pollution control, but it faces numerous risks. Accurately assessing and predicting these risks is essential for ensuring effective water pollution management. This study aims to develop an effective risk classification prediction model for water environment treatment PPP projects, addressing the limitations of traditional methods. First, based on the relevant research on the risk assessment system for water environment treatment PPP projects, a risk data feature set of water environment treatment PPP projects consisting of four subsystems, namely, natural environment, ecological environment, socio-economic, and engineering entity, is proposed. Second, the association between different feature indicators and project risk levels is analyzed from a statistical perspective, and the contribution value of risk features is obtained. Then, an ensemble learning model based on Stack-ing is established to predict the risks of water environment treatment PPP projects. To improve the model's performance, a weighted voting mechanism is designed by introducing weight factors to adjust the relative importance of base learners during the voting process, allowing the model to better exploit the differences between base learners and improve prediction accuracy. Finally, an empirical analysis is conducted on the Phase I project of the comprehensive management of the water environment system in the central urban area of Jiujiang City, China, verifying the effectiveness and accuracy of the risk assessment system and evaluation model constructed in this study. Experimental results show that the constructed Water Environment Treatment Project Risk Support Vector Machine (WETPR-SVM) model outper-forms other traditional single machine learning classification models in terms of accuracy, macro-average precision, macro-average recall, and macro-average value, providing an effective method for risk classification prediction of water environment treatment PPP projects.

In recent years, offshore wind farms, a crucial component of renewable energy, have attracted widespread interest and development worldwide. Nevertheless, offshore wind farms face a variety of meteorological risks during operation, including wind speed fluctuations, strong winds/storms, and extreme storms, which can have a significant impact on the safe operation and stable power generation of wind turbines. Existing methods for predicting meteorological risk frequently lack dynamism and adaptability, failing to meet the requirements of practical applications. This paper proposes a dynamic meteorological risk prediction method for offshore wind power based on hidden Markov models to address this issue. First, we propose four risk states based on the operation of offshore wind turbines under different wind speeds and meteorological conditions: extreme storm events, extreme ocean events, wind speed fluctuations, temperature fluctuations. Then, we construct state transition matrices and output matrices by collecting actual observational data (such as wind speed and wind direction) and combining expert experience and historical events. Finally, we use hidden Markov models to predict the risk states of offshore wind turbines in a dynamic manner. This paper uses artificially generated data to test and compare the performance of the proposed method, demonstrating that it significantly outperforms traditional Markov models and naive Bayes models in state prediction accuracy and is adaptable to some degree. In practical applications, the method can be continuously adjusted and optimized to improve prediction accuracy. By applying the dynamic meteorological risk prediction method for offshore wind power to actual scenarios, wind farm operators can receive real-time information about risks and take the necessary precautions to ensure the safe operation and stable power generation of wind turbines.

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.