Forecasting Groundwater Level Based on Relevance Vector Machine
Relevance Vector Machine (RVM) is a novel kernel method based on sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, and the relevance vectors are automatically determined and have fewer parameters. In this paper, the RVM model is applied to forecasting groundwater level. The experimental results show the final RVM model achieved is sparser, the prediction precision is higher and the prediction values are in better agreement with the real values. It can be concluded that this technique can be seen as a very promising option to solve nonlinear problems such as forecasting groundwater level.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
L. Y. Wang and W. G. Zhao, "Forecasting Groundwater Level Based on Relevance Vector Machine", Advanced Materials Research, Vols. 121-122, pp. 43-47, 2010