径流模拟一直是水文科学的研究热点,对水资源研究具有重要指导意义。基于极限梯度提升算法(XGBoost)和SWAT(Soil and Water Assessment Tool)模型,采用赣江流域外洲水文站的观测水文数据,构建径流模拟模型,对比及分析两个模型的模拟效...径流模拟一直是水文科学的研究热点,对水资源研究具有重要指导意义。基于极限梯度提升算法(XGBoost)和SWAT(Soil and Water Assessment Tool)模型,采用赣江流域外洲水文站的观测水文数据,构建径流模拟模型,对比及分析两个模型的模拟效果。同时,基于XGBoost模型,采用残差校正方法对SWAT模型径流模拟结果进行校正,结果表明:①XGBoost模型的模拟效果较好,XGBoost模型日径流模拟的纳什效率系数(NSE)比SWAT模型高15.66%;②SWAT模型在高流量处存在低估的情况,而XGBoost模型模拟值与观测值变化基本一致,模拟过程线拟合度较高,表现出良好的相关性;③经XGBoost校正后的SWAT模型模拟精度明显提高,可有效改善径流模拟效果。当采用残差误差校正后,径流模拟的NSE值可达0.96,增加了15.66%。展开更多
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model...The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input展开更多
文摘径流模拟一直是水文科学的研究热点,对水资源研究具有重要指导意义。基于极限梯度提升算法(XGBoost)和SWAT(Soil and Water Assessment Tool)模型,采用赣江流域外洲水文站的观测水文数据,构建径流模拟模型,对比及分析两个模型的模拟效果。同时,基于XGBoost模型,采用残差校正方法对SWAT模型径流模拟结果进行校正,结果表明:①XGBoost模型的模拟效果较好,XGBoost模型日径流模拟的纳什效率系数(NSE)比SWAT模型高15.66%;②SWAT模型在高流量处存在低估的情况,而XGBoost模型模拟值与观测值变化基本一致,模拟过程线拟合度较高,表现出良好的相关性;③经XGBoost校正后的SWAT模型模拟精度明显提高,可有效改善径流模拟效果。当采用残差误差校正后,径流模拟的NSE值可达0.96,增加了15.66%。
文摘The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input
