Advanced SearchSearch Tips
Assessment of Scale Effects on Dynamics of Water Quality and Quantity for Sustainable Paddy Field Agriculture
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Environmental Engineering Research
  • Volume 15, Issue 2,  2010, pp.123-126
  • Publisher : Korean Society of Environmental Engineering
  • DOI : 10.4491/eer.2010.15.2.123
 Title & Authors
Assessment of Scale Effects on Dynamics of Water Quality and Quantity for Sustainable Paddy Field Agriculture
Kim, Min-Young; Kim, Min-Kyeong; Lee, Sang-Bong; Jeon, Jong-Gil;
  PDF(new window)
Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ( and ) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.
Scale-dependent modeling;Total nitrogen;Total phosphorus;Rainfall-surface discharge;Modular neural network;Times-eries forecasting;
 Cited by
Shah SMS, O’Connell PE, Hosking JRM. Modelling the effects of spatial variability in rainfall on catchment response. 2. Experiments with distributed and lumped models. J. Hydrol. 1996;175:89-111. crossref(new window)

Chaubey I, Haan CT, Salisbury JM, Grunwald S. Quantifying model output uncertainty due to spatial variability of rainfall. J. Am. Water Res. Assoc. 1999;35:1113-1123. crossref(new window)

El-Sadek A. Upscaling field scale hydrology and water quality modelling to catchment scale. Water Resour. Manage. 2007;21:149-169. crossref(new window)

Karpouzas DG, Capri E. Higher Tier Risk Assessment for pesticides applied in rice paddies: Filling the gap at European level. Outlooks Pest Manage. 2004;15:36-41. crossref(new window)

Band LE, Tague CL, Groffman P, Belt K. Forest ecosystem processes at the watershed scale: Hydrological and ecological controls of nitrogen export. Hydrol. Process. 2001;15:2013-2028. crossref(new window)

Vidstrand P. Comparison of upscaling methods to estimate hydraulic conductivity. Ground Water 2001;39:401-407. crossref(new window)

Rogers L, Johnson V. Groundwater remediation optimization using artificial neural networks. In: Berkeley Initiative in Soft Computing Special Interest Group Earth Sciences Workshop; Mar. 3-6; Berkeley, CA; 1998.

Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 2000;43:3-31. crossref(new window)

Nour MH, Smith DW, El-Din MG, Prepas EE. The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain, with emphasis on the role of wetlands. Ecol. Model. 2006;191:19-32. crossref(new window)

Happel BLM, Murre JMJ. Design and evolution of modular neural network architectures. Neural Networks 1994;7:985-1004. crossref(new window)

Schmidt A, Bandar Z. Modularity-a concept for new neural network architectures. In: IASTED International Conference Computer Systems and Applications (CSA’98); Mar. 3-Apr. 2; Irbid, Jordan; 1998.

Zhang B, Govindaraju RS. Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour. Res. 2000;36:753-762. crossref(new window)

Azam F. Biologically inspired modular neural networks [dissertation].Blacksburg, VA: Virginia Polytechnic Institute and State University; 2000.

Kim HK, Jang TI, Im SJ, Park SW. Estimation of irrigation return flow from paddy fields considering the soil moisture. Agric. Water Manage. 2009;96:875-882. crossref(new window)

Kaastra I, Boyd MS. Forecasting futures trading volume using neural networks. J. Futures Markets 1995;15:953-970. crossref(new window)

Faraway J, Chatfield C. Time series forecasting with neural networks: a comparative study using the air line data. J. Roy. Stat. Soc. Ser. C. (Appl. Stat.) 1998;47:231-250.

Bishop CM. Neural networks for pattern recognition. Oxford:Clarendon Press; 1995.

Gautam RK, Panigrahi S. Development and evaluation of neural network based soil nitrate prediction models from satellite images and non imagery information. American Society of Agricultural and Biological Engineers (ASAE) Annual Meeting; St. Joseph, MI: ASAE; 2004. Paper no. 043108.

Lim G. The linear relationship between monthly precipitation amounts of Korea and time variation of the geopotential height in East Asia and the Pacific during the summer season. J. Korean Meteorol. Soc. 1997;33:63-74.

Lee D, Kim H, Hong S. Heavy rainfall over Korea during 1980-1990. Korean J. Atmos. Sci. 1998;1:32-50.