Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
نویسندگان
چکیده
منابع مشابه
Comparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...
متن کاملcomparison of artificial neural network and regressionpedotransfer functions models for prediction of soil cation exchange capacity in chaharmahal - bakhtiari province
abstract cation exchange capacity (cec) is an important characteristic of soil in terms of nutrient and water holding capacities and contamination management. measurement of cec is laborious and time-consuming. therefore, cec estimation through other easily - measured properties is desirable. in this study, ptfs for estimation of cation exchange capacity from basic soil properties such as parti...
متن کاملBubble Pressure Prediction of Reservoir Fluids using Artificial Neural Network and Support Vector Machine
Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) have been used to predict the bubble point pressure of reservoir fluids. Also, the accuracy of the models have been compared to two-equation stat...
متن کاملComparison of Artificial Neural Network and Multivariate Regression Methods in Prediction of Soil Cation Exchange Capacity
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...
متن کاملPrediction of soil cation exchange capacity using support vector regression optimized by genetic algorithm and adaptive network-based fuzzy inference system
Soil cation exchange capacity (CEC) is a parameter that represents soil fertility. Being difficult to measure, pedotransfer functions (PTFs) can be routinely applied for prediction of CEC by soil physicochemical properties that can be easily measured. This study developed the support vector regression (SVR) combined with genetic algorithm (GA) together with the adaptive network-based fuzzy infe...
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ژورنال
عنوان ژورنال: International Journal of Environmental Science and Technology
سال: 2015
ISSN: 1735-1472,1735-2630
DOI: 10.1007/s13762-015-0856-4