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

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  • Bhattacharjee, Natalia V.
  • Tollner, Ernest W.

Abstract

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.

Suggested Citation

  • Bhattacharjee, Natalia V. & Tollner, Ernest W., 2016. "Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network," Ecological Modelling, Elsevier, vol. 339(C), pages 68-76.
    • Handle: RePEc:eee:ecomod:v:339:y:2016:i:c:p:68-76
    DOI: 10.1016/j.ecolmodel.2016.08.011
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
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    3. Khaled J. Assi & Md Shafiullah & Kh Md Nahiduzzaman & Umer Mansoor, 2019. "Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study," Sustainability, MDPI, vol. 11(16), pages 1-12, August.

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