Spatial Prediction of Dissolved Organic Carbon Using GIS and ANN Modeling in River Networks

Author

Fu, Yingchun ; Zeng, Xiantie ; Lu, Xueyu

Author_Institution

Sch. of Geogr., South China Normal Univ., Guangzhou, China

fYear

2011

fDate

3-4 Dec. 2011

Firstpage

401

Lastpage

406

Abstract

That GIS-based hydrological response units (HRUs) incorporated watershed variables and their potential spatial correlation into ANN modeling was clarified in the paper. The process and final results of neural network modeling were both assessed by the deterministic or statistical methods, spatial regression kriging (RK). The relation of prediction errors and HRUs area scale can provide useful information to optimize the design of stream monitoring network. It is indicated that potential advantage of ANN for watershed and the assessment of estuarine river impacts can be done by precise spatial prediction and sensitive factors analysis.

Keywords

correlation methods; environmental factors; geographic information systems; neural nets; regression analysis; rivers; ANN modeling; GIS-based hydrological response units; deterministic methods; dissolved organic carbon spatial prediction; estuarine river impact assessment; neural network modeling; prediction errors; river networks; sensitive factors analysis; spatial correlation; spatial regression kriging; statistical methods; stream monitoring network; watershed variables; Artificial neural networks; Biological neural networks; Carbon; Correlation; Neurons; Rivers; Soil; artificial neural network (ANN); dissolved organic carbon (DOC); hydrological response units (HRUs); regression kriging(RK);

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2011 Seventh International Conference on

Conference_Location

Hainan

Print_ISBN

978-1-4577-2008-6

Type

conf

DOI

10.1109/CIS.2011.96

Filename

6128055