Modeling groundwater levels in California's Central Valley by
  hierarchical Gaussian process and neural network regression
Anshuman Pradhan Kyra H. Adams Venkat Chandrasekaran Zhen Liu John T. Reager Andrew M. Stuart Michael J. Turmon
Modeling groundwater levels continuously across California's Central Valley
(CV) hydrological system is challenging due to low-quality well data which is
sparsely and noisily sampled across time and space. A novel machine learning
method is proposed for modeling groundwater levels by learning from a 3D
lithological texture model of the CV aquifer. The proposed formulation performs
multivariate regression by combining Gaussian processes (GP) and deep neural
networks (DNN). Proposed hierarchical modeling approach constitutes training
the DNN to learn a lithologically informed latent space where non-parametric
regression with GP is performed. The methodology is applied for modeling
groundwater levels across the CV during 2015 - 2020. We demonstrate the
efficacy of GP-DNN regression for modeling non-stationary features in the well
data with fast and reliable uncertainty quantification. Our results indicate
that the 2017 and 2019 wet years in California were largely ineffective in
replenishing the groundwater loss caused during previous drought years.