Artificial neural networks use for rainfall-runoff erosivity factor estimation

• Autorzy: Licznar P.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

regional climate; artificial neural network; raifall-run-off erosivity factor; soil erosion; climate condition; Universal Soil Loss Equation; water erosion; Revised Universal Soil Loss Equation; estimation

• Wydawca: -
• Czasopismo: Electronic Journal of Polish Agricultural Universities. Series Environmental Development
• Rocznik: 2005
• Tom: 08
• Numer: 1
• Opis fizyczny: http://www.ejpau.media.pl/volume8/issue1/art-04.html

Twórcy

autor

Licznar P.

• Agricultural University of Wroclaw, Pl.Grunwaldzki 24, 50-363 Wroclaw, Poland

Bibliografia

• Banasik K., Górski D.; 1992. Ocena erozyjności deszczy dla trzech wybranych stacji Polski południowo-wschodniej. [Evaluation of rainfall erosivity for three stations in south-east Poland]. Zesz. Nauk. AR we Wrocławiu; Melioracje XL; 211: 39-50; [in Polish].
• Banasik K., Górski D., Mitchell J. K.; 2001. Rainfall erosivity for east and central Poland. Proc. International Symposium & Exhibition on Soil Erosion Research for the 21st Century; Honolulu, Hawaii, USA; 279-282.
• Deumlich D.; 1993. Beitrag zur Erarbeitung einer Isoerodentkarte Deutschlands. [Contribution to an isoerodent map of Germany]. Arch. Acker- Pfl. Boden; 37: 17-24; [in German].
• Deumlich D.; 1999. Erosive Niederschläge und ihre Eintrittswahrscheinlichkeit in Nordosten Deutschlands. [Erosive rainstorms and their probability in Northeast Germany]. Meteorol. Zeitschrift; 8: 155-161; [in German].
• Goovaerts P.; 1999. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena; 34: 227-242.
• Górski D., Banasik K.; 1992. Rozkłady prawdopodobieństwa erozyjności deszczy dla Polski południowo-wschodniej. [Probability distributions of rainfall erosivity for south-east Poland]. Zesz. Nauk. AR w Krakowie; 271: 125-131; [in Polish].
• Licznar P.; 2001. Sieci neuronowe w modelowaniu procesów meteorologicznych. [Neural networks at meteorological processes modeling]. In: Wybrane zagadnienia z zakresu pomiarów i metod opracowania danych automatycznych stacji meteorologicznych. [Some problems the measurements and data processing methodology came from the automatic weather stations]. Eds. J. Łomotowski and M. Rojek. Zesz. Nauk. AR we Wrocławiu; Monografie XXV; 428: 56-79; [in Polish].
• Licznar P.; 2003.: Modelowanie erozji wodnej gleb. [Modeling soil erosion by water]. Zesz. Nauk. AR we Wrocławiu; Monografie XXXII; 456; [in Polish].
• Licznar P., Łomotowski J., Studziński J.; 2002. Anwendung neuronaler Netze zur statistischen Verarbeitung meteorlogischer Datenfolgen aus automatischer Datenerfassung. [Automatic meteorological stations data processing by means of artificial neural networks]. In: Simulation in Umwelt- und Geowissenchaften. [Simulation at environmental- and geosciences]. Eds. J. Wittmann, A. Gnauck. Shaker, Aachen; 9-17, [in German].
• Licznar P., Nearing M. A.; 2003. Artificial neural networks of soil erosion and runoff prediction at the plot scale. Catena 51(2): 89-114.
• Renard K. G., Foster G. R., Weesies G. A., Mccool D. K., Yoder D. C.; 1997. Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agricultural Handbook 703, ARS, Washington, USA.
• Sauerborn P.; 1994. Die Erosivität der Niederschläge in Deutschland - Ein Beitrag zur quantitativen Prognose der Bodenerosion durch Wasser in Mitteleuropa. [Erosivity of rainfalls in Germany - Contribution to quantitative prognosis of soil erosion by water in Middle Europe]. Bonner Bodenkundl. Abh. 13, Bonn [in German].
• Wischmeier W. H., Smith D. D,; 1978. Predicting rainfall erosion losses. A guide to conservation planning. Agricultural Handbook 537, ARS, Washington, USA.