基于非参数核密度估计模型的乌鲁木齐河月径流随机模拟

Urumqi River Monthly Runoff Stochastic Simulation Based on Non-parameter Kernel Density Estimation Model

利用非参数核密度估计方法建立了乌鲁木齐河月径流随机模拟的NP模型.其中,通过以最小二乘交叉验证（LSCV）指标为目标的粒子群优化获取NP模型带宽参数;采用可变核带宽方法进行边界修正.使用1958～2010年间53a月径流数据,经过250组分组模拟进行实用性检验.最后,与使用SAMS2007所建立的季节自回归PAR模型进行了对比.结果表明：所建乌鲁木齐河月径流NP模型能较好保持原序列统计特性;与PAR模型相比,它具有参数少、计算简单的特点.

By using non-parametric kernel density estimation method, a NP stochastic simulation model of Urumqi River monthly runoff was established. Moreover, a particle swarm optimization model using a LSCV （Least Squares Cross- Validation） as the objective function was employed to obtain bandwidth parameters; a variable kernel bandwidth method was adopted to adjust kernel density boundary. Afterwards, 53-year （1958-2010） records of monthly runoff were applied for 250 simulations, each with a length of 53 years, were made to carry on the practicability test of the model. Finally, the results from a PAR （seasonal autoregressive） model built by software SAMS2007 was presented for comparison of NP model. The results show that the Urumqi River monthly runoff NP model can better maintain the statistical properties of the original sequence. Comparing with the PAR model, NP model has the characteristics of fewer parameters and simple calculation.