Neural network models to predict cation exchange capacity in arid regions of Iran

By M. Amini, K. C. Abbaspour, Hossein Khademi, Nader Fathianpour, M. Afyuni, Rainer Schulin. 2005

106 citations recorded by the source corpus.

Subfields: Biomaterials, Civil and Structural Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/dc273b7cc19af9ce2381c9b7268e1b72e97c14c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Samad Emamgholizadeh
2. Hadi Ghorbani
3. Hamed Kashi

In Soil Science

1. Hadi Ghorbani

Citations in the corpus

• Estimation of Soil Cation Exchange Capacity using Multiple Regression, Artificial Neural Networks, and Adaptive Neuro-fuzzy Inference System Models in Golestan Province, Iran (2015, methodology): “Amini et al. (2005) tested several published pedotransfer functions and developed two neural network algorithms using multilayer perceptron (MLP) and general regression neural networks based on a set of 170 soil samples to predict soil cation exchange capacity in central Iran.”
• Modeling soil cation exchange capacity in multiple countries (2017, unknown): “Amini et al. (2005) found that sand and silt did not have a signi fi cant e ff ect in predicting CEC.”
• Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models (2014, methodology): “Amini et al. (2005) estimated the cation exchange capacity (CEC) in central Iran using soil organic matter and texture.”