At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow and it is difficult to meet the needs of daily flood control and emergency. How to use artificial intelligence technology to quickly predict urban flooding is a key concern and a problem that needs to be solved. Therefore, this paper combines a numerical model with good computational accuracy and an LSTM artificial neural network model with high computational efficiency to propose a new method for fast prediction of urban flooding risk. The method uses the simulation results of the numerical model of urban flooding as the data driver to construct the LSTM neural network prediction model of each waterlogging point. The results show that the method has a high prediction accuracy and fast calculation speed, which can meet the needs of daily flood control and emergency response, and provides a new idea for the application of artificial intelligence technology in the direction of flood prevention and mitigation.

Precipitation is a vital component of the regional water resource circulation system. Accurate and efficient precipitation prediction is especially important in the context of global warming, as it can help explore the regional precipitation pattern and promote comprehensive water resource utilization. However, due to the influence of many factors, the precipitation process exhibits significant stochasticity, uncertainty, and nonlinearity despite having some regularity. In this article, monthly precipitation in Zhoukou City is predicted using a complementary ensemble empirical modal decomposition (CEEMD) method combined with a long short-term memory neural network (LSTM) model and a least squares support vector machine (LSSVM) model. The results demonstrate that the CEEMD-LSTM-LSSVM model exhibits a root mean square error of 15.01 and a mean absolute error of 11.31 in predicting monthly precipitation in Zhoukou City. The model effectively overcomes the problems of modal confounding present in empirical modal decomposition (EMD), the existence of reconstruction errors in ensemble empirical modal decomposition (EEMD), and the lack of accuracy of a single LSTM model in predicting modal components with different frequencies obtained by EEMD decomposition. The model provides an effective approach for predicting future precipitation in the Zhoukou area and predicts monthly precipitation in the study area from 2023 to 2025. The study provides a reference for relevant departments to take effective measures against natural disasters and rationally plan urban water resources.

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.

This paper addresses the issue of water surface fluctuations in aqueducts caused by the Karman Vortex Street phenomenon, which significantly impacts the structural stability and water delivery efficiency of the aqueducts. Based upon existing research findings, a representative three-dimensional fluid dynamics model is developed to optimize the transition section and tail pier structure of the aqueduct, with the objective of reducing water level fluctuations and improving hydraulic stability.The research focuses on improving the symmetry of both the transition section and the tail pier, optimizing the tail pier structure, and analyzing its wave attenuation effect. The experimental results demonstrate that a conical tail pier performs better than a platform-shaped tail pier in eliminating water surface fluctuations, significantly reducing turbulent energy dissipation at the exit transition section, maintaining a relatively stable Froude number, and achieving a stable flow pattern. Furthermore, by comparing the total head loss at the outlet tapering section, it is found that a shorter (e.g.,15-meter) conical tail pier results in lower head loss than a longer (e.g.,35-meter) conical tail pier, indicating that a compact conical tail pier is more effective. This suggests that shorter tail piers are generally more effective in reducing head loss and improving flow stability. These findings offer valuable insights for optimizing tail pier selection in aqueduct design.This study highlights the crucial role of tail pier structure in reducing water surface fluctuations caused by the Karman Vortex Street phenomenon, thereby providing theoretical support and practical guidance for improving the structural stability and water delivery efficiency of aqueducts.