Forecast of water level and ice jam thickness using the back propagation neural network and support vector machine methods

Ice jams can sometimes occur in high latitude rivers during winter and the resulting water level rise may generate costly and dangerous flooding such as the recent ice jam flooding in the Nechako River in downtown Prince George in Canada. Thus, the forecast of water level and ice jam thickness is of great importance. This study compares three methods to simulate and forecast water level and ice jam thickness based on field observations of river ice jams in the Quyu Reach of the Yellow River in China. More specifically, simulation results generated by the traditional multi-variant regressional method are compared to those of the back propagation neural network and the support vector machine methods. The forecast of ice jam thickness and water level under ice jammed condition have been conducted in two different approaches, 1] simulation of water level and ice jam thickness in the second half of the period of measurement using models developed based on data gained during the first half of the period of measurement, 2] simulation of water level and ice jam thickness at the downstream cross sections using models developed based on data gained at the upstream cross sections. For this reason, as the results of simulation and field observations indicated, the back propagation neural network method and the support vector machine method are superior in terms of accuracy to the multi-variant regressional method

Flow and high sediment yield from the Huangfuchuan watershed

This study deals with general features of sediment transport in the Huangfuchuan river, an important branch river of the Yellow river, draining the Loess Plateau in China. The long-term precipitation records at 3 climate stations near the watershed have been used to assess the changes in precipitation in this region. By introducing metrics for runoff and sediment transport, long-term observations on discharge and sediment concentration at the Huangfuchuan gauging station on the Huangfuchuan river have been used to assess the changes in runoff and sediment transport in the Huangfuchuan River compared with those at the Toudaoguai and Fugu gauging stations on the Yellow river. It is found that both runoff and sediment metrics have a clearly downward trend. From July to August, the long-term sediment transport modulus at the Huangfuchuan gauging station is much higher than those at the Toudaoguai and Fugu stations on the Yellow river. The long-term average sediment transport modulus at the Huangfuchuan gauging station are high, ranging from 14000. t/[km2 y] but decreasing to 5900 t/[km2 y] over the last 10 years. Additionally, analyses on grain size distribution of suspended load in the Huangfuchuan and Yellow river have been conducted