Method for predicting nitrogen concentration in water on lower Mississippi River in USA
Yan Baowen1, Mark D. Tomer2, Wen Deping3
1.College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; 2.National Laboratory for Agriculture and the Environment, ARS/USDA, AMES, IA 50011-0000, USA; 3.Water and Hydrologic Survey Bureau of Qinghai Province, Dulan, Qinghai 816100, China
Abstract:In order to investigate pollution and eutrophication caused from agricultural nonpoint source pollution in river water, it is essential to predict the relation between the concentration of nitrate nitrogen in a river and the runoff for such an ion-dominated pollutant. Hence, the lower Mississippi river with intensive farming land in USA was chosen as a model and the nitrate nitrogen concentration, runoff water quality data collected from Vicksburg Hydrological Station on the river were analyzed. Then the whole runoff data set was separated into daily and monthly individual data sets by using Baseflow program, furthermore, the individual runoff data sets were extended and complemented by using Loadrunner Program of Yale University to form a continuously daily nitrate nitrogen concentration series; eventually, the monthly concentration sequences were established. The monthly nitrate nitrogen concentration in the water body was predicted by means of neural network method, as a result, the corresponding procedure and predication formulas were proposed. The results showed that the average error between predicted and observed concentrations is 7.5%, implying the procedure and formulas proposed are accurate and feasible. It suggests that this method can be applied to predict monthly nitrate nitrogen concentration in a real river.
作者简介: Mark D. Tomer(1960—),男,美国爱荷华州人,研究员,博士(Mark.Tomer@ARS.USDA.GOV),主要从事土壤环境与水环境保护研究.
引用本文:
严宝文, Mark D. Tomer, 温得平. 美国密西西比河下游水体含氮量预报方法[J]. 排灌机械工程学报, 2013, 31(9): 800-804.
Yan Baowen, Mark D. Tomer, Wen Deping. Method for predicting nitrogen concentration in water on lower Mississippi River in USA. Journal of Drainage and Irrigation Machinery Engin, 2013, 31(9): 800-804.
[1]Goolsby D A, Battaglin W A. Nitrogen in the Mississippi basin—Estimating sources and predicting flux to the gulf of Mexico[R].US Geological Survey Fact Sheet,2000:1-6.[2]Wu Guoyuan.Evaluation of water qulaty of the lower Mississppii river[J].Chinese Journal of Oceanology and Limnology,1984,2(2):194-208.[3]严黎,吴门伍,李杰.密西西比河的防洪经验及其启示[J].中国水利,2010(5):55-57. Yan Li,Wu Menwu,Li Jie.Experience of Mississippi ri-ver flood control and its enlightenment[J].China Water Resource,2010(5):55-57.(in Chinese)[4]Zhu Yuanhong, Day R L.Regression modeling of streamflow,baseflow,and runoff using geographic information systems[J]. Journal of Environmental Management,2009,90(2):946-953.[5]Eckhardt K. How to construct recursive digital filters for baseflow separation[J].Hydrological Processes,2005,19(2):507-515.[6]Load Estimator.A Fortran Program for Estimating Constituent Loads in Streams and Rivers[M].US:John′s Press,2009.[7]Fernando T M K G, Maier H R, Dandy G C. Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach[J].Journal of Hydrology,2009,367(3/4):165-176.[8]缪益平,邓俊.基于BP人工神经网络的枯水径流预报方案研究[J].水文,2008,28(3):33-37. Liao Yiping,Deng Jun. Research on low-flow forecasting scheme based on BP artificial neural network[J]. Journal of China Hydrology,2008,28(3):33-37.(in Chinese).[9]May R J, Maier H R, Dandy G C, et al. Non-linear variable selection for artificial neural networks using partial mutual information[J]. Environmental Modelling & Software, 2008, 23(10/11):1312-1326.