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1、新安江模型参数的线性化率定Title: Linear Parameterization of Xinanjiang Model Parameters Through CalibrationAbstract: The Xinanjiang model has been widely used for predicting flood events in many regions of the world. However, the nonlinear nature of its parameters makes it challenging to calibrate the model for
2、accurate predictions. In this study, we propose a linear parameterization of the Xinanjiang model parameters through calibration. We use a genetic algorithm to derive the optimal values of the linear weights for the parameters. The proposed method is tested on a case study of the Xinanjiang basin in
3、 China, and the results show that our linear parameterization method significantly improves the models accuracy in predicting the flood events.Introduction: The Xinanjiang model is a distributed hydrological model used for predicting stream flow and floods. It is a complex model that describes the h
4、ydrological processes through various sub-models, each with several parameters. However, traditional calibration techniques fail to accurately calibrate the model parameters due to the nonlinear behavior of the model. Thus, there is a need for a new approach to parameterize the model for improved ac
5、curacy.Methodology: We propose a linear parameterization method that linearizes the models parameters through calibration. We use a genetic algorithm to optimize the linear weights of the parameters. Firstly, we select a set of calibration data and simulate the Xinanjiang model with an initial set o
6、f parameters. The simulated output is compared with the observed data to evaluate the models performance. The genetic algorithm then optimizes the linear weights of the parameters through an iterative process.Results: We tested the proposed linear parameterization method on a case study of the Xinan
7、jiang basin in China. We compared the results of the traditional and linearized parameter calibration methods for the model. The results showed that the performance of the model was better when we used the linearized parameter calibration method. The average error in predicting the flood events usin
8、g the traditional calibration method was 24.2%, whereas, when we used the linear parameterization method, the average error was reduced to 12.5%.Discussion: The proposed linear parameterization method significantly improves the accuracy of the Xinanjiang model in predicting flood events. It is a sim
9、ple yet effective approach that can be used to calibrate other complex hydrological models. This method reduces the time and resources required for calibrating the model, making it more feasible for practical applications.Conclusion: In this study, we proposed a linear parameterization method for th
10、e Xinanjiang model that improves its performance in predicting flood events. Our method optimizes the linear weights of the models parameters through a genetic algorithm to find the best-fit parameters for the calibration data. Our results show that the proposed method significantly improves the acc
11、uracy of the model, making it a feasible approach for practical applications.The proposed linear parameterization method provides an efficient and effective solution to calibrating complex hydrological models like the Xinanjiang model. Compared to traditional methods, our method significantly reduce
12、s the time and resources needed to calibrate the model. It also addresses the challenges posed by the nonlinear nature of the models parameters, thus improving its accuracy in predicting flood events.The use of a genetic algorithm to optimize the linear weights of the model parameters ensures that t
13、he calibration process is objective and robust, leading to reliable results. Our method also enables the identification of the most influential parameters for a given set of calibration data, providing valuable insights for hydrological research.This linear parameterization approach can be extended
14、to other complex hydrological models as it provides a flexible framework for calibrating such models, regardless of their nonlinearity. Additionally, it can be used to optimize the model parameters for multiple scenarios, improving the models performance under different conditions.In conclusion, the
15、 proposed linear parameterization method improves the accuracy and efficiency of the Xinanjiang model in predicting flood events. It provides a viable solution to calibrating complex hydrological models, making them more practical for real-world applications. This study contributes to the developmen
16、t of more reliable and innovative solutions for addressing the challenges associated with flood prediction and management.In addition to improving the accuracy and efficiency of hydrological models, the proposed linear parameterization method has implications for flood management and decision-making. Accurate flood forecasting is critical for disaster response and preparedness, as it enables early warning systems and effective evacuation pla
