Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

Impoundment Impact of the Three Gorge Reservoir on the Hydrological Regime in the Lower Han River, China

Reservoir regulation has been playing an increasingly important role in water resources development and its influence on the hydrological processes of downstream tributaries has attracted much attention. The lower Han River is selected as a case study to examine the hydrological and hydraulic influence of the upstream flow regulation of the Three Gorges Reservoir (TGR) in the middle Yangtze River, China. Based on a hydrodynamic model and the observed data, the hydrological processes in the lower Han River were simulated and their changes were analyzed under the impoundment influences of the TGR. The results indicated that there were obviously hydrological changes in the lower Han River after the TGR operation. The decreased stage downstream the TGR during the impounding periods of the TGR resulted in an increase in the stage difference, current speed, hydraulic gradient and the discharge ratio. In addition, the stage difference between the two rivers was decreased during the periods of water compensation from the TGR, which led to the outflow congestion in the lower Han River. The hydrological changes in the lower Han River were the response to the flow regulation of the TGR and the inflow of the two rivers. The variation in the rating curve in the lower Han River mainly resulted from the stage difference between the two rivers during the dispatching periods of the TGR. These results help to explain the hydrological variability under the impounding influence of the TGR for the lower Han River and they can be extended to other river tributaries downstream to the reservoirs.

Factors to Affect the Eutrophication and Phytoplankton Blooms in the Three Gorges Reservoir

The phytoplankton blooms in the tributary bays have become the most serious environmental problem in Three Gorge Reservoir (TGR), China. In order to understand the mechanism of those phytoplankton blooms, the factors to affect the eutrophication and Phytoplankton blooms have been summarized. Generally, in lakes eutrophication is a natural evolutionary process, which is affected by light, temperature, nutrients, predation, migration and stratification. Recent decade's human activities played a role to promote this evolution but not to change the evolutionary direction. However, in the TGR, the changed hydrodynamics caused by the TGD are considered to be the most important triggers to the phytoplankton blooms, especially the longer residence time, thermal density currents and stratification.