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

• 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
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2013.07.001

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 quantified by the mean weight diameter, MWD) in a highly mountainous watershed (Bazoft watershed, southwestern Iran). Three different sets of easily available properties were used as inputs. The first set (denoted as SP) consisted of soil properties including clay content, calcium carbonate equivalent, and soil organic matter content. The second set (denoted as TVA) included topographic attributes (slope and aspect) and the normalized difference vegetation index (NDVI). The third set (denoted as STV) was a combination of soil properties, slope, and NDVI. The ANN and ANFIS models predicted MWD more accurately than the GLM and MLR models. Estimation of MWD using TVA data set resulted in the lowest model efficiency values. The observed model efficiency values for the developed MLR, GLM, ANN, and ANFIS models using the SP data set were 60.76, 62.98, 77.68 and 77.15, respectively. Adding slope and NDVI to soil data (i.e. STV data set) improved the predictions of all four methods. The obtained correlation coefficient values between the predicted and measured MWD for the developed MLR, GLM, ANN, and ANFIS models using STV data set were 0.24, 0.35, 0.84 and 0.73, respectively. In conclusion, the ANN and ANFIS models showed greater potential in predicting soil aggregate stability from soil and site characteristics, whereas linear regression methods did not perform well.
•The study was carried out in a highly mountainous watershed.•The main objective was determining the best intelligence method for MWD prediction.•Three different sets of easily available properties were used as inputs.•The ANN and ANFIS models showed greater potential in predicting MWD.•The linear methods did not perform well.