Neural network modelling of rainfall interception in four different forest stands

• Main Authors: İbrahim Yurtseven, Mustafa Zengin
• Format: Article
• Language: English
• Published: ‘Marin Drăcea’ National Research-Development Institute in Forestry 2013-11-01
• Series: Annals of Forest Research
• Subjects: artificial neural network (ANN); throughfall; stemflow; interception; forest stands
• Online Access: http://www.editurasilvica.ro/afr/56/2/yurtseven.pdf

The objective of this study is to reveal whether it is possible to predict rainfall, through fall and stem flow in forest ecosystems with less effort, using several measurements of rainfall interception (hereafter ‘interception’) and an artificial neural network based linear regression model (ANN model). To this end, the Kerpe Research Forest in the province of Kocaeli, which houses stands of mixed deciduous-broadleaf forest (Castanea sativa Mill., Fagusorientalis Lipsky, Quercus spp.), black pine (Pinus nigra Arnold), maritime pine (Pinus pinaster Aiton) and Monterey pine (Pinus radiata D. Don), was selected study site. Four different forest stands were observed for a period of two years, during which rainfall, throughfall and stemflow measurements were conducted. These measurements were separately calculated for each individual stand, based on interception values and the use of stemflow data in strict accordance with the rainfall data, and the measured throughfall interceptionvalues were compared with values estimated by the ANN model.In this comparison, 70% of the total data was used for testing, and 30% was used for estimation and performance evaluation. No significant differences were found between values predicted with the help of the model and the measured values. In other words, interception values predicted by the ANN models were parallel with the measured values. In this study, the most success was achieved with the models of the Monterey pine stand (r2 = 0.9968; Mean Squared Error MSE = 0.16) and the mixed deciduous forest stand (r2 = 0.9964; MSE = 0.08), followed by models of the maritime pine stand (r2 = 0.9405; MSE = 1.27) and the black pine stand (r2 = 0.843, MSE = 17.36).