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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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 95; S. 173 - 180
Hauptverfasser:	Keshavarzi, Ali, Sarmadian, Fereydoon, Shiri, Jalal, Iqbal, Munawar, Tirado-Corbalá, Rebecca, Omran, El-Sayed Ewis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 01.01.2017 Elsevier Science Ltd
Schlagworte:	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
Online-Zugang:	Volltext
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Abstract	•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.
AbstractList	•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. 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.
Author	Keshavarzi, Ali Iqbal, Munawar Shiri, Jalal Sarmadian, Fereydoon Omran, El-Sayed Ewis Tirado-Corbalá, Rebecca
Author_xml	– sequence: 1 givenname: Ali surname: Keshavarzi fullname: Keshavarzi, Ali email: alikeshavarzi@ut.ac.ir organization: Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box: 4111, Karaj 31587-77871, Iran – sequence: 2 givenname: Fereydoon surname: Sarmadian fullname: Sarmadian, Fereydoon organization: Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box: 4111, Karaj 31587-77871, Iran – sequence: 3 givenname: Jalal surname: Shiri fullname: Shiri, Jalal organization: Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran – sequence: 4 givenname: Munawar surname: Iqbal fullname: Iqbal, Munawar organization: Department of Chemistry, Qurtuba University of Science and Information Technology, Peshawar 25100, KPK, Pakistan – sequence: 5 givenname: Rebecca surname: Tirado-Corbalá fullname: Tirado-Corbalá, Rebecca organization: Department of Agro-Environmental Sciences, University of Puerto Rico-Mayagüez, PR, USA – sequence: 6 givenname: El-Sayed Ewis surname: Omran fullname: Omran, El-Sayed Ewis organization: Soil and Water Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
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Keywords	Soil Cation Exchange Capacity Multi variable linear regression ANFIS Subtractive clustering Soil properties
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Snippet	•The conventional procedures for soil Cation Exchange Capacity (CEC) measurement are time consuming.•MR and ANFIS models were employed to predict the soil CEC... The conventional procedures for soil Cation Exchange Capacity (CEC) measurement are time consuming and laborious. It is also difficult to maintain stability...
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SubjectTerms	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
Title	Application of ANFIS-based subtractive clustering algorithm in soil Cation Exchange Capacity estimation using soil and remotely sensed data
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