Al-Hamed, S., Wahby, M., Al-Sulaiman, M., Al-Saqer, S. and Aboukarima, A. (2012) Application of Artificial Neural Networks to Predict Natural Soil Radionuclides Concentrations in Some Saudi Agricultural Soils. Archives Des Sciences, 65, 196-206.

has been cited by the following article:

• TITLE: 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

  AUTHORS: Saad Al-Hamed, Mohamed Wahby, Mohamed Al-Sulaiman, Abdulwahed Aboukarima

  KEYWORDS: Agriculture, ANFIS, Soil Texture, Natural Radionuclides

  JOURNAL NAME: Open Journal of Soil Science, Vol.4 No.7, July 10, 2014

  ABSTRACT: 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 (40K), Uranium (238U), Thorium (232Th) and Cesium (137Cs) 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.