Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation results of the hydrodynamic model as the data driver, a neural network structure combining convolutional neural network (CNN) and long and short-term memory network (LSTM) is constructed, taking into account rainfall factors, geographical data, and the distribution of the drainage network. The study was carried out with the central city of Zhoukou as an example. The results show that after the training of the hydrodynamic model and CNN−LSTM neural network model, it can quickly predict the depth of urban flooding in less than 10 s, and the average error between the predicted depth of flooding and the measured depth of flooding does not exceed 6.50%, which shows that the prediction performance of the neural network is good and can meet the seeking of urban emergency flood control and effectively reduce the loss of life and property.

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

In order to investigate the influence of different design rainfall peaks on urban flooding characteristics based on the MIKE hydrodynamic model, the inundation process scenarios were extrapolated for different recurrence periods and three single- and double-peak rainfall types in Zhoukou city as an example, and the equivalent values of total inundation and inundation area were compared and analysed. The results show that bimodal rainfall has a higher risk of inundation than unimodal rainfall for the same rainfall ephemeris and return period. For unimodal rainfall, when the return period is less than 20 years, the more advanced the rainfall peak, the more severe the design rainfall inundation. When the return period is greater than 20 years, the further back the rainfall peak, the more severe the inundation of the design rainfall. The difference between the risk of inundation due to single- and double-peaked rainfall decreases as the return period increases.

The prediction of wave transformation and associated hydrodynamics is essential in the design and construction of reef top structures on fringing reefs. To simulate the transformation process with better accuracy and time efficiency, a shock-capturing numerical model based on the extended Boussinesq equations suitable for rapidly varying topography with respect to wave transformation, breaking and runup, is established. A hybrid finite volume–finite difference scheme is used to discretize conservation form of the extended Boussinesq equations. The finite-volume method with a HLL Riemann solver is applied to the flux terms, while finite-difference discretization is applied to the remaining terms. The fourth-order MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) scheme is employed to create interface variables, with in which the van-Leer limiter is adopted to improve computational accuracy on complex topography. Taking advantage of van-Leer limiter, a nested model is used to take account of both computational run time and accuracy. A modified eddy viscosity model is applied to better accommodate wave breaking on steep reef slopes. The established model is validated with laboratory measurements of regular and irregular wave transformation and breaking on steep fringing reefs. Results show the model can provide satisfactory predictions of wave height, mean water level and the generation of higher harmonics.

Superstandard conditions refer to extreme rainfall and river flooding that exceed the flood control system or flood control works. In order to explore the influence of superstandard conditions on urban waterlogging, based on the MIKE FLOOD platform, this paper takes the downtown area of Zhoukou as the research object, and it conducts waterlogging simulation in this area under the extreme rainfall conditions of once in 20 years and once in 50 years combined with the ultra-high water level of the river. The simulation results of inundation depth, inundation range and drainage capacity of a self-flowing river are compared and analyzed. The results show that heavy rainfall is the main cause of waterlogging disaster. When extreme rainfall and river flood occur at the same time, the artesian drainage capacity of the pipeline is seriously affected due to the high water level of the river. The city has a large amount of water, and when the river embankment overflows, it poses a serious threat to the safety of the city.

The form of the inlet section of aqueducts that connect the upstream channel and the downstream channel affects the flow pattern and head loss. In order to provide a reference for the design of the gradient section of water-transfer channels, a typical three-dimensional hydrodynamic model is established in this paper based on existing results. The results show that the local head loss coefficient is related to the cross-sectional area of the inlet and outlet of the gradient section, the water surface contraction angle of the gradient section, and the elevation difference between the bottoms of the inlet and outlet of the gradient section, and a functional relationship is provided; when changing the width of the inlet and outlet bottoms, the local head loss coefficient is negatively related to the water surface contraction angle and increases with the increase in W_up/W_down; the local head loss coefficient has a good exponential function with W_up/W_down. The research results can provide a reference for the design of the inlet gradient section and the solution of the head loss coefficient.