Application of ANFIS-based subtractive clustering algorithm in soil Cation Exchange Capacity estimation using soil and remotely sensed data

•The conventional procedures for soil Cation Exchange Capacity (CEC) measurement are time consuming.•MR and ANFIS models were employed to predict the soil CEC based on clay, organic carbon (OC) and Band 3 inputs.•The results revealed that the ANFIS model had the ability to estimate soil CEC by compu...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 95; s. 173 - 180
Hlavní autoři: Keshavarzi, Ali, Sarmadian, Fereydoon, Shiri, Jalal, Iqbal, Munawar, Tirado-Corbalá, Rebecca, Omran, El-Sayed Ewis
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Elsevier Ltd 01.01.2017
Elsevier Science Ltd
Témata:
Adaptive systems
Algorithms
ANFIS
Artificial neural networks
Cation exchanging
Clustering
Computer simulation
Fuzzy logic
Fuzzy sets
Fuzzy systems
Ion exchange
Multi variable linear regression
Regression analysis
Regression models
Remote sensing
Reproducibility
Sensitivity analysis
Soil Cation Exchange Capacity
Soil investigations
Soil properties
Soils
Subtractive clustering
Soil Cation Exchange Capacity
Multi variable linear regression
ANFIS
Subtractive clustering
Soil properties
ISSN:0263-2241, 1873-412X
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Shrnutí:•The conventional procedures for soil Cation Exchange Capacity (CEC) measurement are time consuming.•MR and ANFIS models were employed to predict the soil CEC based on clay, organic carbon (OC) and Band 3 inputs.•The results revealed that the ANFIS model had the ability to estimate soil CEC by computing easily measurable variables. The conventional procedures for soil Cation Exchange Capacity (CEC) measurement are time consuming and laborious. It is also difficult to maintain stability for long-term experiments and projects. Therefore, this study aimed at comparing Adaptive Neuro-Fuzzy Inference System (ANFIS)-based subtractive clustering algorithm with different inputs combinations as well as sequential regression models for simulation of variations in soil CEC. Results showed that the corresponding values of root mean squared error (RMSE) and coefficient of determination (R2) between the measured and simulated CEC using the best regression equation and ANFIS models were 2.05 and 0.733, 1.35 and 0.806, respectively. Nevertheless, sensitivity analysis was conducted to determine the most and the least influential variables affecting soil CEC. Results of the present investigation showed that the ANFIS model had the ability to estimate soil CEC by computing easily measurable variables with guarantee of authenticity, reliability and reproducibility.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2016.10.010