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

The recurrent neural network is a tool that can provide valuable insights when forecasting future likelihood of events using dynamic time series. One of the challenging research problems is to extend the black-box modeling into white-box modeling in order to gain insights into the physical processes. Sensitivity analysis has shown a great contribution in overcoming this challenge. The main objective of this study was to perform a detailed sensitivity analysis of recurrent neural network in order to identify parameters that are important for predicting water quality constituents. We used a windrow composting pad located at UGA Bioconversion center, Athens, GA, USA as our study site. Runoff from windrow composting pad was collected in order to prevent the discharge of organic pollutants. We used time series data from nine years of precipitation, temperature, pond volume, material volume on the pad, total suspended solids (TSS), biological oxygen demand (BOD) and nitrate (NO3) concentration levels of stored runoff in the collection pond. 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. (C) 2016 Elsevier B.V. All rights reserved.