# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
However, existing studies have given insufficient consideration to the extent of flooding caused by different factors when assigning weights to indicators, and the lack of optimization of the indicator system has limited the objectivity of the assessment results. The scenario simulation method based on hydrodynamic models cannot provide a comprehensive risk assessment of flooding due to the lack of potential surface and socio-economic factors; remote-sensing-based techniques require high classification accuracy and data matching [8]. Using a single-weighting method can easily lead to insufficient accuracy in assessing urban flood risk characteristics. The optimal values of the indicators it determines are somewhat subjective and uncertain. The modeling method of the machine learning model requires a large number of parameters, and insufficient or incorrect parameters can affect the accuracy of the data. The indicator system approach is the most widely used because it can show the flood risk situation in macro-regions or larger-scale regions, and the calculation method is relatively simple. In the indicator system method, the allocation of indicator weights is a key step in determining the accuracy of risk assessment, and the commonly used indicator assignment methods can be divided into two types: subjective and objective assignment methods. The subjective assignment method represented by the AHP method analyzes more from a qualitative perspective and relies too much on expert knowledge, which limits the objectivity of the assessment results; the objective assignment method represented by the entropy weight method is easily affected by the distribution of the original data, and it is difficult to ensure the stability of the assessment results. This study adopts the subjective-objective combination assignment method to assess the risk of disaster-causing factors, the CRITIC-entropy weighting method to measure the exposure of disaster-containing environments and the vulnerability of disaster-carrying bodies, and the AHP subjective assignment method to examine the criterion layer. Additionally, the subjective-objective combination of the assignment method means that subjective factors can be combined with objective factors so as to arrive at a quicker and more accurate decision-making result [41; 42; 43]. Compared with the scenario simulation method of the hydrodynamic model, the subjective-objective combination assignment method has a wide calculation range and can include surface and social factors. Compared with the use of remote sensing technology to collect data for the assessment of flood disasters, the subjective-objective combination assignment method does not need high-precision data to complete the assessment of flood disaster risk. Compared with the single-assignment method, the subjective-objective combination assignment method has fully taken into account the role of experts' experience and objective data to make the results more accurate, and compared with the machine learning model, it does not need a large amount of parameter support to complete the assessment quickly and accurately. Compared with the single assignment method, the subjective-objective combination assignment method can complete the assessment quickly and accurately without the support of a large number of parameters. However, combined assignment methods still cannot completely avoid the influence of the original data distribution, so there are potential limitations. In view of this, this study takes Liaocheng City as the 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.