Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas

  • Authors:

  • Minyoung Kim;John E. Gilley

  • Affiliations:

  • National Institute of Agricultural Engineering, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon 441-707, Republic of Korea;USDA-ARS, Agroecosystem Management Research Unit, 120 Keim Hall, East Campus, University of Nebraska, Lincoln, NE 68583-0934, USA

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2008

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Abstract

The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANNs) trained with a backpropagation (BP) algorithm were used to estimate soil erosion, dissolved P (DP) and NH"4-N concentrations of runoff from a land application site near Lincoln, Nebraska, USA. Simulation results from ANN-derived models showed that the amount of soil eroded is positively correlated with rainfall and runoff. In addition, concentrations of DP and NH"4-N in overland flow were related to measurements of runoff, EC and pH. Coefficient of determination values (R^2) relating predicted versus measured estimates of soil erosion, DP, and NH"4-N were 0.62, 0.72 and 0.92, respectively. The ANN models derived from measurements of runoff, electrical conductivity (EC) and pH provided reliable estimates of DP and NH"4-N concentrations in runoff.