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A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment

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  • Anctil, François
  • Filion, Mélanie
  • Tournebize, Julien

Abstract

In this paper, we report an application of neural networks to simulate daily nitrate-nitrogen and suspended sediment fluxes from a small 7.1km2 agricultural catchment (Melarchez), 70km east of Paris, France. Nitrate-nitrogen and sediment losses are only a few possible consequences of soil erosion and biochemical applications associated to human activities such as intensive agriculture. Stacked multilayer perceptrons models (MLPs) like the ones explored here are based on commonly available inputs and yet are reasonably accurate considering their simplicity and ease of implementation. Note that the simulation does not resort on water quality flux observations at previous time steps as model inputs, which would be appropriate, for example, to predict the water chemistry of a drinking water plant a few time steps ahead. The water quality fluxes are strictly mapped to historical mean flux values and to hydro-climatic variables such as stream flow, rainfall, and soil moisture index (12 model input candidates in total), allowing its usage even when no flux observations are available. Self-organizing feature maps based on the network structure established by Kohonen were employed first to produce the training and the testing data sets, with the intent to produce statistically close subsets so that any difference in model performance between validation and testing has to be associated to the model and not to the data subsets. The stacked MLPs reached different levels of performance simulating the nitrate-nitrogen flux and the suspended sediment flux. In the first instance, 2-input stacked MLP nitrate-nitrogen simulations, based on the same-day stream flow and on the 80-cm soil moisture index, have a performance of almost 90% according to the efficiency index. On the other hand, the performance of 3-input stacked MLPs (same-day stream flow, same-day historical flux, and same-day stream flow increment) reached a little more than 75% according to the same criterion. The results presented here are deemed already promising enough, and should encourage water resources managers to implement simple models whenever appropriate.

Suggested Citation

  • Anctil, François & Filion, Mélanie & Tournebize, Julien, 2009. "A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment," Ecological Modelling, Elsevier, vol. 220(6), pages 879-887.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:6:p:879-887
    DOI: 10.1016/j.ecolmodel.2008.12.021
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    References listed on IDEAS

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    1. Sharma, V. & Negi, S. C. & Rudra, R. P. & Yang, S., 2003. "Neural networks for predicting nitrate-nitrogen in drainage water," Agricultural Water Management, Elsevier, vol. 63(3), pages 169-183, December.
    2. Gassman, Philip W. & Reyes, Manuel R. & Green, Colleen H. & Arnold, Jeffrey G., 2007. "The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions," ISU General Staff Papers 200701010800001027, Iowa State University, Department of Economics.
    3. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.
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    2. Kichul Jung & Myoung-Jin Um & Momcilo Markus & Daeryong Park, 2020. "Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    3. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.

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