مقایسه مدل شبکه عصبی مصنوعی و رگرسیون خطی چندمتغیره در پیش بینی کربن آلی خاک به کمک داده های آنالیز سطح زمین

Abstract

Abstract Spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring CO2 and preventing soil erosion strategies within the landscape, regional, and global scale. The objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain attributes. A study area of 24 km2 in hilly regions of Zargham Ababd in south of Semirom under natural rangeland uses, was selected and then 125 soil samples (0-10 cm depth) were collected. Soil organic carbon was measured for the collected soil samples. Topographic attributes were calculated by a digital elevation model with 10 m spacing. Finally, multiple linear regression (MLR) analysis and ANN models were developed for soil organic carbon estimation in the study area and then the developed modeless were validated by additional samples (25 points). The results showed that the MLR and ANN models explained 60 and 89 % of the total variability of SOC, respectively, in the study area using terrain attributes. Sensitivity analysis based upon the ANN models, revealed that the profile curvature, stream power index, slope, sediment transport index, wetness index, plan curvature and aspect were identified as the important topographic attributes influencing the SOC distribution within the selected hillslope. The overall results indicated that topographic attributes and hydrological process control a significant variability of SOC. Prediction of the statistical studied models in the study area resulted in mean error and root mean square error values of 0.25, 0.3 in MLR equation and 0.006, 0.027 in ANN, respectively. Therefore, the ANN model could provide superior predictive performance when compared with developed MLR model. Keywords: Soil organic carbon, Terrain attributes, Linear regression, Artificial neural network