Against the backdrop of global warming and rising sea levels coupled with increasing urbanization, flood risks for plain cities have intensified. This study takes Liaocheng City as its research object and constructs a regional flood risk assessment model based on a combination of subjective and objective multi-weight methods. The model sets weights according to different return periods from three perspectives: the severity of disaster-causing factors, the exposure of disaster-prone environments, and the vulnerability of disaster-bearing bodies. It also uses a subjective–objective combination of weights for the severity of disaster-causing factors, adopts CRITIC-entropy weights for the exposure of disaster-prone environments and vulnerability of disaster-bearing bodies, and adopts AHP subjective weights for the criterion layer. Based on GIS spatial analysis technology, the examination and zoning of flood disasters at a county scale were carried out. The results show that, unlike the existing weighting methods and machine learning methods, this multi-weight combination method can simultaneously avoid the subjectivity of the results and the uncertainty of parameters, thus enabling more accurate decision-making results to be obtained. The spatial distribution of the comprehensive risk is high in the central and western parts and relatively low in the south and north, while the area characterized by very high risk is concentrated in Dongchangfu District and Guanxian County. With the gradual increase in return periods, the overall spatial distribution of medium-to-very-high-risk areas in risk zoning gradually shrinks, and the spatial distribution of very-high-risk areas gradually moves south but maintains a stable distribution rule. Flood risk assessment is an important basic process for disaster prevention and mitigation in plain cities, and the results of this study can provide a reference for similar plain cities.

Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries.

This study integrated rule-based control (RBC) and permeable pavements to assess the combined effectiveness in reducing the combined sewer overflow (CSO) and energy consumption of the urban drainage system (UDS). Non-dominated sorting was employed to identify the optimal RBC preset parameters under varying rainfall intensities and permeable pavement installation proportion. Cost-effectiveness was calculated for different RBC parameters and permeable pavement installation scenarios. The contribution of installing permeable pavements in each sub-catchment to the cost-effectiveness was quantified using frequency statistics. The combined effectiveness of the RBC and LID achieved maximum reduction rates of 51.12% to 72.46% in the CSO volume and 14.75% to 33.89% in energy consumption across return periods from 2-year to 20-year. The key findings were as follows: (1) Optimization of the RBC preset parameters can reduce the CSO volume and energy consumption, but the effectiveness is limited by the capacity of the UDS. (2) In the UDS, the permeable pavement installation area and return periods of design rainfall significantly impact the optimization of RBC. (3) The impact of the RBC parameters on the cost-effectiveness of installing permeable pavements in each sub-catchment is minimal. (4) The cost-effectiveness of installing permeable pavement varies across different sub-catchments.