IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v220y2009i6p879-887.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380008005978
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2008.12.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Imteaz, Monzur Alam & Ahsan, Amimul & Rahman, Ataur & Mekanik, Fatemeh, 2013. "Modelling stormwater treatment systems using MUSIC: Accuracy," Resources, Conservation & Recycling, Elsevier, vol. 71(C), pages 15-21.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
    2. Sarangi, A. & Singh, Man & Bhattacharya, A.K. & Singh, A.K., 2006. "Subsurface drainage performance study using SALTMOD and ANN models," Agricultural Water Management, Elsevier, vol. 84(3), pages 240-248, August.
    3. Liu, Xiaozhi & Kang, Shaozhong & Li, Fusheng, 2009. "Simulation of artificial neural network model for trunk sap flow of Pyrus pyrifolia and its comparison with multiple-linear regression," Agricultural Water Management, Elsevier, vol. 96(6), pages 939-945, June.
    4. 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.
    5. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    6. Egbendewe-Mondzozo, Aklesso & Swinton, Scott M. & Bals, Bryan D. & Dale, Bruce E., 2011. "Can Dispersed Biomass Processing Protect the Environment and Cover the Bottom Line for Biofuel?," Staff Paper Series 119348, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    7. Andersson, Jafet C.M. & Zehnder, Alexander J.B. & Rockström, Johan & Yang, Hong, 2011. "Potential impacts of water harvesting and ecological sanitation on crop yield, evaporation and river flow regimes in the Thukela River basin, South Africa," Agricultural Water Management, Elsevier, vol. 98(7), pages 1113-1124, May.
    8. Hongxing Liu & Wendong Zhang & Elena Irwin & Jeffrey Kast & Noel Aloysius & Jay Martin & Margaret Kalcic, 2020. "Best Management Practices and Nutrient Reduction: An Integrated Economic-Hydrologic Model of the Western Lake Erie Basin," Land Economics, University of Wisconsin Press, vol. 96(4), pages 510-530.
    9. Medwid, Laura J. & Lambert, Dayton M. & Clark, Christopher D. & Hawkins, Shawn A. & McClellan, Hannah A., 2016. "Estimating Soil Loss Abatement Curves with Primary Survey Data and Hydrologic Models: An Empirical Example for Livestock Production in an East Tennessee Watershed," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230052, Southern Agricultural Economics Association.
    10. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2016. "Research Needs and Challenges in the FEW System: Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems," Center for Agricultural and Rural Development (CARD) Publications 16-wp563, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    11. Alan F. Hamlet & Nima Ehsani & Jennifer L. Tank & Zachariah Silver & Kyuhyun Byun & Ursula H. Mahl & Shannon L. Speir & Matt T. Trentman & Todd V. Royer, 2024. "Effects of climate and winter cover crops on nutrient loss in agricultural watersheds in the midwestern U.S," Climatic Change, Springer, vol. 177(1), pages 1-21, January.
    12. Negar Tayebzadeh Moghadam & Karim C. Abbaspour & Bahram Malekmohammadi & Mario Schirmer & Ahmad Reza Yavari, 2021. "Spatiotemporal Modelling of Water Balance Components in Response to Climate and Landuse Changes in a Heterogeneous Mountainous Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 793-810, February.
    13. Yates, Andrew J. & Doyle, Martin W. & Rigby, J.R. & Schnier, Kurt E., 2013. "Market power, private information, and the optimal scale of pollution permit markets with application to North Carolina's Neuse River," Resource and Energy Economics, Elsevier, vol. 35(3), pages 256-276.
    14. Eini, Mohammad Reza & Salmani, Haniyeh & Piniewski, Mikołaj, 2023. "Comparison of process-based and statistical approaches for simulation and projections of rainfed crop yields," Agricultural Water Management, Elsevier, vol. 277(C).
    15. Jeong, Hanseok & Kim, Hakkwan & Jang, Taeil & Park, Seungwoo, 2016. "Assessing the effects of indirect wastewater reuse on paddy irrigation in the Osan River watershed in Korea using the SWAT model," Agricultural Water Management, Elsevier, vol. 163(C), pages 393-402.
    16. S. K. Aryal & S. Ashbolt & B. S. McIntosh & K. P. Petrone & S. Maheepala & R. K. Chowdhury & T. Gardener & R. Gardiner, 2016. "Assessing and Mitigating the Hydrological Impacts of Urbanisation in Semi-Urban Catchments Using the Storm Water Management Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5437-5454, November.
    17. Lingcheng Li & Liping Zhang & Jun Xia & Christopher Gippel & Renchao Wang & Sidong Zeng, 2015. "Implications of Modelled Climate and Land Cover Changes on Runoff in the Middle Route of the South to North Water Transfer Project in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2563-2579, June.
    18. Kotchakarn Nantasaksiri & Patcharawat Charoen-Amornkitt & Takashi Machimura, 2021. "Land Potential Assessment of Napier Grass Plantation for Power Generation in Thailand Using SWAT Model. Model Validation and Parameter Calibration," Energies, MDPI, vol. 14(5), pages 1-15, March.
    19. Howard, Gregory E. & Zhang, Wendong & Valcu-Lisman, Adriana M., 2021. "Evaluating the Efficiency-Participation Tradeoff in Agricultural Conservation Programs: The Effect of Reverse Auctions, Spatial Targeting, and Higher Offered Payments," 2021 Annual Meeting, August 1-3, Austin, Texas 313926, Agricultural and Applied Economics Association.
    20. Sanjeet Kumar & Ashok Mishra, 2015. "Critical Erosion Area Identification Based on Hydrological Response Unit Level for Effective Sedimentation Control in a River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1749-1765, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:220:y:2009:i:6:p:879-887. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.