Uncertainty-Based Deep Learning Networks for Limited Data Wetland User Models

This paper discusses a method for dealing with limited data in deep networks based on calculating the uncertainty associated with remaining training data. The method was developed for the Watershed REstoration using Spatio-Temporal Optimization of REsources (WRESTORE) system, an interactive decision support system designed for performing multi-criteria decision analysis with a distributed system of conservation practices on the Eagle Creek Watershed in Indiana, USA. Our results show faster and more stable convergence when using an uncertainty-based incremental sampling method than when using a standard random incremental sampling method. This work describes the existing WRESTORE system, provides details about the implementation of our uncertainty-based incremental sampling method, and provides a discussion of our results and future work. The primary contribution of the paper is an uncertainty-based incremental sampling method which can be applied to limited data watershed design problems.