Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression

Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD) of aggregates, and water-dispersible clay (WDC) content (Calero et al., 2008). Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010). Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014). The main objective of this study is to investigate the potential use of support vector machines (SVMs) method for estimating soil aggregate stability (as quantified by GMD) as compared to multiple linear regression approach. Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E), which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM) content was determined by the Walkley-Black method (Nelson & Sommers, 1986). Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand) were measured using the procedure described by Gee & Bauder (1986) and calcium carbonate equivalent (CCE) content was determined by the back-titration method (Nelson, 1982). The modified Kemper & Rosenau (1986) method was used to determine wet-aggregate stability (GMD). The topographic attributes of elevation, slope, and aspect were characterized using a 20-m by 20-m digital elevation model (DEM). The data set was divided into two subsets of training and testing. The training subset was randomly chosen from 70% of the total set of the data and the remaining samples (30% of the data) were used as the testing set. The correlation coefficient (r), mean square error (MSE), and error percentage (ERROR%) between the measured and the predicted GMD values were used to evaluate the performance of the models. Results and Discussion: The description statistics showed that there was little variability in the sample distributions of the variables used in this study to develop the GMD prediction models, indicating that their values were all normally distributed. The constructed SVM model had better performance in predicting GMD compared to the traditional multiple linear regression model. The obtained MSE and r values for the developed SVM model for soil aggregate stability prediction were 0.005 and 0.86, respectively. The obtained ERROR% value for soil aggregate stability prediction using the SVM model was 10.7% while it was 15.7% for the regression model. The scatter plot figures also showed that the SVM model was more accurate in GMD estimation than the MLR model, since the predicted GMD values were closer in agreement with the measured values for most of the samples. The worse performance of the MLR model might be due to the larger amount of data that is required for developing a sustainable regression model compared to intelligent systems. Furthermore, only the linear effects of the predictors on the dependent variable can be extracted by linear models while in many cases the effects may not be linear in nature. Meanwhile, the SVM model is suitable for modelling nonlinear relationships and its major advantage is that the method can be developed without knowing the exact form of the analytical function on which the model should be built. All these indicate that the SVM approach would be a better choice for predicting soil aggregate stability. Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future

Identification and prioritization of critical sub-basins in a highly mountainous watershed using SWAT model

A few areas in a large watershed might be more critical and responsible for high amount of runoff and soil losses. For an effective and efficient implementation of watershed management practices, identification of these critical areas is vital. In this study, we used the Soil and Water Assessment Tool (SWAT, 2009) to identify and prioritize the critical sub-basins in a highly mountainous watershed with imprecise and uncertain data (Bazoft watershed, southwestern Iran). Three different SWAT models were first developed using different climate input data sets. The first data set (denoted as CRU) was derived from the climate research unit data set developed by the British Atmosphere Data Center (BADC). The second data set (denoted as CDW) was included the climate data obtained from the precipitation and air temperature stations in the study area. The third set (denoted as COM) was a combination of CRU and CDW climate data. The Generalized Likelihood Uncertainty Estimation (GLUE) program was used for calibrating and validating the SWAT model. Daily rainfall, temperature, and runoff data of 20 years (1989-2008) were used in this study. In results, the constructed SWAT model using COM data set simulated the runoff more satisfactorily than the two other developed SWAT models according to the statistical evaluation criteria. The correlation coefficient and Nash-Sutcliff values for the constructed SWAT model using COM data set were 0.40 and 0.38, respectively. The model simulated the runoff satisfactorily; however, the predicted runoff values were much more in agreement with the measured data for the calibration period than those for the validation period. Sub-basins S10, S12, and S13 were assigned as the most top critical sub-basins in runoff production in the watershed. The study revealed that the SWAT model could successfully be used for identifying the critical sub-basins in a watershed with imprecise and uncertain data for management purposes

Land use planning based on SWAT model in a mountainous watershed to reduce runoff and sediment load

Soil erosion is a major environmental threat to the sustainability and productive capacity of soils. This study aimed to identify optimal land use types for Zayandehrood watershed in central Iran for the first time which is large and mountainous to minimize runoff production and soil loss. Two different types of land use data for two scenarios were developed using Soil and Water Assessment Tool (SWAT) in combination with Sequential Uncertainty Fitting Program (SUFI-2) at the sub-basin level with uncertainty analysis to explicitly quantify hydrological components on a daily time step. In the first Scenario, the current land use map of the study area was used and the second Scenario was constructed using an optimal land use map obtained from a land evaluation study. Promotion of the land uses in the second scenario resulted in a noticeable reduction in discharge and sediment productions in the watershed. The simulated mean discharge values by the Scenarios 1 and 2 were about 14,658 and 13,290 m3/year, respectively. The mean annual sediment yield simulated by the Scenario 1 (about 122,220 ton/year) decreased to that of the Scenario 2 (94,440 ton/year). This study provides a strong basis for reducing runoff and sediment yields in central Iran; however, its general analytical framework could be applied to other parts of the world that are facing similar challenges.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satellite images obtained from Google Earth. Five data sets with pixel sizes ranging from 2 to 30 m were obtained using topographic attributes and remote sensing data comprising the elevation, slope degree, slope aspect, catchment area, plan curvature, profile curvature, stream power index, topographic position index, topographic wetness index, land use, and normalized difference vegetation index, which can affect the distribution of gully erosion. For each data set, 70% and 30% of the data were selected randomly for calibrating and validating the models, respectively. The statistical relationships between the occurrence of gully erosion and controlling factors were calculated using four machine-learning models, i.e., generalized linear model, boosted regression tree (BRT), multivariate adaptive regression spline, and artificial neural network (ANN). Statistical tests comprising the kappa coefficient and the area under the receiver operating characteristic curve (AUC) were calculated for both the calibration and validation data sets to estimate the optimal pixel size. The results showed that among the data sets with different pixel sizes, the optimal pixel size was 10 m for each model. In addition, the capacity of the four techniques for modeling gully erosion occurrence was quite stable when the calibration and validation samples were changed in the data set. Finally, based on three changes of the calibration and validation data sets with a pixel size of 10 m, the BRT and ANN models obtained outstanding performance (AUC > 0.9), where they had the highest goodness-of-fit and predictive power, and thus the greatest robustness to changes in the calibration/validation data (i.e., lowest sensitivity to altering calibration/validation data). Our results demonstrate the importance of selecting a suitable pixel size when producing a GESM for soil and water management practices