Development of artificial neural network to predict greenhouse gas emissions from fields with different water regimes

Authors

  • Chusnul Arif Department of Civil and Environmental Engineering, Bogor Agricultural University
  • Budi Indra Setiawan Department of Civil and Environmental Engineering, Bogor Agricultural University
  • Slamet Widodo Department of Mechanical and Biosystem Engineering, Bogor Agricultural University
  • - Rudiyanto Department of Civil and Environmental Engineering, Bogor Agricultural University
  • Nur Aini Iswati Hasanah Department of Civil and Environmental Engineering, Bogor Agricultural University
  • Masaru Mizoguchi Department of Global Agricultural Sciences, The University of Tokyo

DOI:

https://doi.org/10.31028/ji.v10.i1.1-10

Keywords:

artificial neural networks, environmental biophysics, irrigation system, greenhouse gas emissions, paddy fields

Abstract

The paper proposes the artificial neural networks (ANN) model to predict methane (CH4) and Nitrous Oxide (N2O) emissions under different irrigation system based on easily measurable environmental biophysics parameters such as soil moisture, soil temperature and soil electrical conductivity. To verify the model, two experiments were conducted in the pot experiments in two different locations. The first location was in the greenhouse of Meiji University, Kanagawa Prefecture, Japan from 4 June to 21 September 2012, and the second location was in water resources engineering laboratory, Department of Civil and Environmental Engineering-IPB from 2 July to 10 October 2014. In each location, there were three different irrigation systems adopted with the System of Rice Intensification (SRI) principles. We called the experiment as SRI Basah (SRI B1 and SRI B2 for the first and second locations, respectively), SRI Sedang (SRI S1 dan SRI S2) dan SRI Kering (SRI K1 dan SRI K2). Each treatment has different water level during growth stages. As the results, the developed ANN model can predict CH4 and N2O emissions accurately with determination coefficients of 0.93 and 0.70 for CH4 and N2O prediction, respectively. From the model, characteristics of those greenhouse gas emissions can be well identified. For the mitigation strategy, SRI B1 and SRI B2 treatments in which the water level was kept at nearly soil surface are the best strategy with highest yield production and lowest GHG emission.

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Published

2015-04-15

How to Cite

Arif, C., Setiawan, B. I., Widodo, S., Rudiyanto, .-., Hasanah, N. A. I., & Mizoguchi, M. (2015). Development of artificial neural network to predict greenhouse gas emissions from fields with different water regimes. Jurnal Irigasi, 10(1), 1–10. https://doi.org/10.31028/ji.v10.i1.1-10

Issue

Vol. 10 No. 1 (2015): Jurnal Irigasi

Section

Articles

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