GIS and ANN-based spatial prediction of DOC in river networks: a case study in Dongjiang, Southern China

• Published in: Environmental earth sciences 2013-03, Vol.68 (5), p.1495-1505
• Main Authors: Fu, Yingchun, Zhao, Yaolong, Zhang, Yongrui, Guo, Taisheng, He, Ziwei, Chen, Jingyi
• Format: Article
• Language: English
• Subjects: Agricultural land; algorithms; Anthropogenic factors; Applied geophysics; Areal geology. Maps; Biogeosciences; Case studies; Dissolved organic carbon; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environmental Science and Engineering; Exact sciences and technology; Freshwater; Geochemistry; Geographic information systems; Geologic maps, cartography; Geology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Internal geophysics; kriging; longitude; moderate resolution imaging spectroradiometer; Original Article; prediction; River networks; river water; Rivers; runoff; shrublands; soil organic carbon; Statistical methods; Surface runoff; Terrestrial Pollution; Urban areas; Watersheds
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-012-2177-y

This paper investigates the use of an artificial neural network (ANN) model to predict dissolved organic carbon (DOC) in a river network and evaluates the impacts of watershed characteristics on stream DOC. Samples and relevant environmental variables were obtained from field sampling at 28 hydrological response units (HRUs) and a MODIS/SRTM DEM satellite image. HRUs can provide reliable spatial interpolation for filling data gaps and incorporate potential spatial correlation among observations in each ANN neuron. The process and results of neural network modeling were assessed by deterministic and statistical methods and spatial regression kriging. The spatial prediction results show that ANN, using improved back propagation algorithms of 7-15-1 architecture, was the optimal network, by which predictions maintained most of the original spatial variation and eliminated smoothing effects of RK. The sum of the relative contributions of four sensitive variables, including soil organic carbon density, geographic longitude, surface runoff and Chl a in river water, was >75 %. A minor prediction error of ~6 % was found in HRUs of open shrublands, but HRUs of urban and croplands had an error of 24–30 %. This pattern exemplifies anthropogenic impacts in urban areas on stream DOC and agricultural activities in croplands. The usefulness of ANN modeling-based GIS in this study is demonstrated by depiction of spatial variation of stream DOC and indicates the benefits of understanding sensitive factors for watershed impact assessments.