中文摘要：

小浪回归(WR ) 模型通常被使用 hydrologic 时间系列预报，但是他们不能考虑无常评估。在这糊 AM-MCMC (适应 Metropolis-Markov 链蒙特卡罗) 算法被采用到回归的建模处理的小浪，并且打电话给 AM-MCMC-WR 的一个模型为 hydrologic 时间系列预报被建议。AM-MCMC 算法被用来在 WR 模型， hydrologic 时间系列的概率的预报能基于被做估计参数无常。在 Huaihe 河分水岭的二个流量数据的结果显示在获得最佳的预报的 AM-MCMC-WR 和 WR 模型的相同表演结果，但是他们比线性回归模型更好表演。不同于 WR 模型，概率的预报结果能被建议模型获得，并且无常能用合适的可信的间隔被描述。在摘要，在 WR 模型的参数通常跟随正常概率分发；系列关联字符决定最佳的参数值，并且进一步决定参数的不明确的度和敏感；更不明确的参数将导致更多的不明确的预报结果和 hydrologic 时间系列的难可预测性。

英文摘要：

Wavelet regression （WR） models are used commonly for hydrologic time series forecasting, but they could not consider uncer- tainty evaluation. In this paper the AM-MCMC （adaptive Metropolis-Markov chain Monte Carlo） algorithm was employed to wavelet regressive modeling processes, and a model called AM-MCMC-WR was proposed for hydrologic time series forecasting. The AM-MCMC algorithm is used to estimate parameters＇ uncertainty in WR model, based on which probabilistic forecasting of hydrologic time series can be done. Results of two runoff data at the Huaihe River watershed indicate the identical performances of AM-MCMC-WR and WR models in gaining optimal forecasting result, but they perform better than linear regression models. Differing from the WR model, probabilistic forecasting results can be gained by the proposed model, and uncertainty can be de- scribed using proper credible interval. In summary, parameters in WR models generally follow normal probability distribution; series＇ correlation characters determine the optimal parameters values, and further determine the uncertain degrees and sensitivi- ties of parameters; more uncertain parameters would lead to more uncertain forecasting results and hard predictability of hydro- logic time series.