Use of machine learning methods to reduce predictive error of groundwater models.

Quantitative analyses of groundwater flow and transport typically rely on a physically-based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data-driven models (DDMs) to reduce the predictive error of physically-based groundwater models. Two machine learning techniques, the instance-based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real-world case studies of the Republican River Compact Administration model and the Spokane Valley-Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root-mean-square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically-based model.

Human-Android Interaction in the Near and Distant Future.

In this article, a psychologist and an artificial-intelligence (AI) researcher speculate on the future of social interaction between humans and androids (robots designed to look and act exactly like people). We review the trajectory of currently developing robotics technologies and assess the level of android sophistication likely to be achieved in 50 years time. On the basis of psychological research, we consider obstacles to creating an android indistinguishable from humans. Finally, we discuss the implications of human-android social interaction from the standpoint of current psychological and AI research and speculate on the novel psychological issues likely to arise from such interaction. The science of psychology will face a remarkable new set of challenges in grappling with human-android interaction.