基于径流模数的LSTM模型在无资料嵌套流域的应用

针对LSTM模型参数在无资料嵌套流域移植效果不佳的问题,以屯溪流域为研究对象,以其嵌套子流域为参证流域,综合考虑面积因素对参数移植法的影响,建立了基于径流模数的LSTM模型(RM-LSTM模型),并采用参数移植法进行流域洪水过程模拟。结果表明:RM-LSTM模型在研究流域应用效果较好,模拟结果的确定性系数达0.87,洪峰、洪量及峰现时间合格率均在85.0%以上；在参证子流域内,RM-LSTM模型参数移植后模拟结果的确定性系数及洪峰、洪量、峰现时间合格率较LSTM模型参数移植结果均有明显提升；RM-LSTM模型能够更好地考虑流域面积变化对参数移植方法的影响。

Application of runoff modulus-based LSTM in ungauged nested watersheds

To address the problem that the parameters of the LSTM model do not transpose well in nested watersheds with inadequate records of hydrological observations, a runoff modulus-based LSTM(RM-LSTM) was developed using the Tunxi Watershed as the study watershed and its nested sub-basins as the reference watersheds, with the consideration of the influence of area factor on the parameter transfer method. The flood simulations in the reference watersheds were carried out using the parameter transfer method. The results showed that the RM-LSTM model was well applied in the study watershed, with a determination coefficient of 0.87 for the simulation results, and a qualification rate of 85.0% or more for flood peak, flood volume and peak present time. In the reference watersheds, the RM-LSTM model showed a marked improvement in terms of the determination coefficient, qualification rate of flood peak, qualification rate of flood volume, and qualification rate of peak time after the parameter transplantation, compared to the results obtained from the LSTM model. The RM-LSTM model can better take into account the influence of watershed area changes on the parameter transfer method.