Prediction of Soil Fractions (Sand, Silt and Clay) in Surface Layer Based on Natural Radionuclides Concentration in the Soil Using Adaptive Neuro Fuzzy Inference System — Oak Academic Publishing
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Prediction of Soil Fractions (Sand, Silt and Clay) in Surface Layer Based on Natural Radionuclides Concentration in the Soil Using Adaptive Neuro Fuzzy Inference System
Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA
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Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA
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Community College, Huraimla, Shaqra University, Huraimla, KSA
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Agricultural Engineering Research Institute, Agricultural Research Centre, Cairo, Egypt
1 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA
3 Community College, Huraimla, Shaqra University, Huraimla, KSA
4 Agricultural Engineering Research Institute, Agricultural Research Centre, Cairo, Egypt
In this research, a gamma ray sensor (The Mole) was used to get the natural radionuclides concentration in situ in the surface layer of cultivated soils. For sand, silt and clay predictions, an adaptive neuro fuzzy inference system (ANFIS) was performed to predict such fractions (Sugeno model). The inputs to the system were Potassium ( 40 K), Uranium ( 238 U), Thorium ( 232 Th) and Cesium ( 137 Cs) concentrations. It is concluded that ANFIS structure is acceptable in the prediction of sand, silt and clay considering the studied inputs. Test results and predicted outcomes were compared and acceptable correlations were obtained.
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