Investigation of influencing factors for valley deformation of high arch dam using machine learning

During the operation period of the high arch dam in some canyon area, the valley deformation is obvious under complex geological environment. Several possible factors may influence this phenomenon. In this article, influencing factors are considered from the aspects of precipitation, temperature, reservoir water level elevation and the rate of reservoir water level variation. Two machine learning methods are employed to investigate these factors, namely, Lasso and Random forest, and serve as a basis for further studying the formation mechanism of valley deformation. Using the Lasso method, four variations are selected from 74 variations which are representative of precipitation and temperature, e.g. daily precipitation, 10-day antecedent precipitation, 10-day maximum rainfall difference, and 25-day maximum temperature difference. Combined with the reservoir water level elevation and the rate of reservoir water level variation, these six key factors are analyzed by the Random forest method so to carry out a quantitative analysis of their influence on valley deformation. Results show that the valley deformation is mainly affected by the rate of reservoir water level variation and the reservoir water level elevation among the selected factors, indicating valley deformation might be induced with hydrodynamic force.