Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network

Previously, we applied recurrent neural network for predicting TSS, BOD and NO3 as well as performed auto-correlation and cross-correlation analysis (Shim and Tollner, 2014). We used first eight years of data (from January 2001 to December 2008) to build the model and last year of data (from January 2009 to December 2009) to evaluate the model. Within this paper, we showed that the detailed sensitivity analysis of recurrent neural network can allow a better understanding of water quality dynamics of collected runoff and assist in identifying strategies for better management of windrow composting systems.