EJPAU 2005. Licznar P. ARTIFICIAL NEURAL NETWORKS USE FOR RAINFALL-RUNOFF EROSIVITY FACTOR ESTIMATION

Electronic Journal of Polish Agricultural Universities (EJPAU) founded by all Polish Agriculture Universities presents original papers and review articles relevant to all aspects of agricultural sciences. It is target for persons working both in science and industry,regulatory agencies or teaching in agricultural sector. Covered by IFIS Publishing (Food Science and Technology Abstracts), ELSEVIER Science - Food Science and Technology Program, CAS USA (Chemical Abstracts), CABI Publishing UK and ALPSP (Association of Learned and Professional Society Publisher - full membership). Presented in the Master List of Thomson ISI.

2005
Volume 8
Issue 1

Topic:

Environmental Development

ELECTRONIC
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES

Licznar P. 2005. ARTIFICIAL NEURAL NETWORKS USE FOR RAINFALL-RUNOFF EROSIVITY FACTOR ESTIMATION, EJPAU 8(1), #04.
Available Online: http://www.ejpau.media.pl/volume8/issue1/abs-04.html

ARTIFICIAL NEURAL NETWORKS USE FOR RAINFALL-RUNOFF EROSIVITY FACTOR ESTIMATION

Paweł Licznar
Institute of Building and Landscape Architecture, Agricultural University of Wroclaw, Poland

ABSTRACT

Proposed by Wischmeier and Smith rainfall-runoff erosivity factor (R-factor) is usually recognized as a proper tool for regional climatic condition description in respect to soil erosion by water. It is also a basic input to simple and widespread soil erosion prediction models like USLE and RUSLE. However its calculation on the base of original precipitation records is a very laborious operation and is completely impossible for many locations without a precise precipitation data. The aim of the research was to develop a new simple method of annual R-factor values estimation on the base of very general precipitation data. Examined was the possibility of implementing artificial neural networks for annual R-factor values estimation on the base of the sole summer period and annual precipitation totals. The research was conducted with the use of database containing calculated summer period and annual rainfall-runoff erosivity factor values from 138 stations in Germany. As a result of the study 3 radial basis function networks (RBF) of two to five hidden layer neurons and 2 multilayer perceptrons networks (MLP) with one and two hidden layers were developed. Obtained correlation coefficients of observed versus predicted R-factor values were higher then the coefficients reported previously for the simple linear regression models. The study results suggested the possibility of neural networks technology introduction for R-factor values estimation on the base of precipitation totals instead of simple statistical regional relationships.

Key words: Artificial neural networks, rainfall-runoff erosivity factor, estimation.

Paweł Licznar
Institute of Building and Landscape Architecture,
Agricultural University of Wroclaw, Poland
Plac Gruwaldzki 24, 50-363 Wrocław, Poland
Phone (048)-71-3482-850
email: licznarp@ozi.ar.wroc.pl

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