Search Results (3)

The concept of sponge cities is widely recognized, but there is still no research on what a new drainage system for sponge cities should look like. This study proposes a new drainage system for sponge cities, a sponge-type comprehensive pipe corridor rainwater chamber (SCRC) system, which combines a comprehensive pipe corridor with low-impact development measures (LIDs) into one system. The SCRC system is predicted by using a long- and short-term neural network to verify whether the neural network can be applied to the prediction of flooding in sponge cities. The results show that the SCRC system can effectively control sponge city flooding, where the surface runoff coefficient under different rainfall intensities (P = 1–10 yr) is between 0.273 and 0.44, the pipe overload time is between 0.11 and 3.929 h, and the node overflow volume is between 0 and 23.89 Mltr. The neural network has a high reliability in sponge city flood prediction, and the coefficients of determination R² of the test set of PSO–LSTM prediction models are all above 0.95. This study may provide an idea for predicting flooding in sponge cities. Full article

Currently, most of the studies using optimization algorithms to mitigate the urban flooding problem have no more than three optimization objectives, and few of them take the operation status of the traditional drainage system as one of the optimization objectives, which is not conducive to the overall design of the city. This study proposes to mitigate the urban flooding problem by using a sponge-type comprehensive pipe corridor rainwater chamber. A four-objective optimization model is established by coupling the Storm Water Management Model (SWMM) and the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), and two traditional drainage system state indicators (pipe overload time, node overflow volume), surface runoff coefficient, and total investment cost are selected as the optimization objectives for solving the problem. The results show that (1) the reduction rates of surface runoff coefficient, pipe overload time, and node overflow volume rate by the optimization model are 37.015–56.917%, 81.538–91.435%, and 51.578–84.963%, respectively; and the total investment cost is RMB 4.311–4.501 billion. (2) The effectiveness of combining SWMM and NSGA-III for an optimization solution is verified, and the relationship between the four objectives is explored. The study may provide useful information for urban flood control. Full article

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors. Full article