| Home | E-Submission | Sitemap | Contact Us |  
Environmental Engineering Research 2010;15(2): 123-126. DOI: https://doi.org/10.4491/eer.2010.15.2.123
Assessment of Scale Effects on Dynamics of Water Quality and Quantity for Sustainable Paddy Field Agriculture
Minyoung Kim, Minkyeong Kim, Sangbong Lee, and Jonggil Jeon
Department of Agricultural Engineering, National Academy of Agricultural Science, Rural Development Administration, Suwon 441-707, Korea
Corresponding Author: Minkyeong Kim ,Tel: +82-31-290-0223, Fax: +82-31-290-0206, Email: kimmk72@korea.kr
Received: December 15, 2009;  Accepted: February 22, 2010.
Share :  
ABSTRACT
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 (1.5 × 105 and 1.62 × 106 m2) 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 rainfallsurface 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.
Keywords: Scale-dependent modeling | Total nitrogen | Total phosphorus | Rainfall-surface discharge | Modular neural network | Timeseries forecasting
TOOLS
PDF Links  PDF Links
Full text via DOI  Full text via DOI
Download Citation  Download Citation
  E-Mail
Share:      
METRICS
0
Crossref
0
Scopus
1,022
View
2
Download
Editorial Office
464 Cheongpa-ro, #726, Jung-gu, Seoul 04510, Republic of Korea
TEL : +82-2-383-9697   FAX : +82-2-383-9654   E-mail : eer@kosenv.or.kr

Copyright© Korean Society of Environmental Engineers. All rights reserved.        Developed in M2community
About |  Browse Articles |  Current Issue |  For Authors and Reviewers