NDLI: The use of Kohonen neural networks for runoff

Content Provider	Springer Nature Link
Author	Farias, Camilo Allyson Simões Santos, Celso Augusto Guimarães
Copyright Year	2014
Abstract	We investigated the application of Kohonen Neural Networks (KNNs) in order to estimate sediment yield based on runoff and climatological data in a semiarid region of Brazil. Accurate estimations of sediment yield are essential to improve the management of soil erosion in semiarid areas, where large quantities of sediments tend to be produced only periodically.The case study is an erosion plot within the São João do Cariri Experimental Basin, which is located in the semiarid portion of Paraíba State, Brazil. KNNs are unsupervised neural networks capable of reducing a multidimensional data set to a bidimensional matrix of features, which can be used for analysis and prediction purposes. A total of 60 rainfall events, which occurred between 1999 and 2002, were used to calibrate and test the model. The application of a multivariate linear regression (MLR) model was also carried out.Statistical indexes were used as criteria for evaluating the performance of the KNN and MLR models for the test data set. The correlation and relative bias of the KNN model estimations with those from observed data were 0.90 and −4.39 %, respectively. A correlation of 0.70 and a relative bias of 15.63 % were found from the comparison of sediment yields obtained by the MLR model with those of the observed data. Analysis of the outcomes indicates that the KNN model, which is capable of detecting and extracting nonlinear trends, produced more reliable results than the regression model.The KNN model results appear to be superior to those generated by the MLR model and suggest that the developed methodology may be applied to similar case studies.
Starting Page	1242
Ending Page	1250
Page Count	9
File Format	PDF
ISSN	14390108
Journal	Journal of Soils and Sediments
Volume Number	14
Issue Number	7
e-ISSN	16147480
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-01-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Sediment yield Self-organizing maps Semiarid region Soil Science & Conservation Environment Environmental Physics
Content Type	Text
Resource Type	Article
Subject	Stratigraphy Earth-Surface Processes

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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