Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed

A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), generalized linear model (GLM), and multiple linear regression (MLR) models are appropriate for prediction of soil wet aggregate stability (as qua...

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Bibliographic Details
Published in:	Catena (Giessen) Vol. 111; pp. 72 - 79
Main Authors:	Besalatpour, A.A., Ayoubi, S., Hajabbasi, M.A., Mosaddeghi, M.R., Schulin, R.
Format:	Journal Article
Language:	English
Published:	Cremlingen-Destedt Elsevier B.V 01-12-2013 Catena
Subjects:	aggregate stability Aggregates Artificial neural networks Bgi / Prodig calcium carbonate Catchments. Hydrological cycle clay fraction Conventional regression models correlation data collection fuzzy logic Hydrometeorology Intelligence systems Learning theory linear models Mathematical models Mean weight diameter Neural networks Neuro-fuzzy inference system normalized difference vegetation index Physical geography prediction Regression regression analysis Soil (material) soil organic matter Stability Watersheds Iran Central Iran Conventional regression models Mean weight diameter Neuro-fuzzy inference system Intelligence systems Artificial neural networks Soil erosion Vegetation index Mountain Methodology Soil properties Topography Watershed Model Aggregate Water erosion Comparative study
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