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.

The unsaturated hydraulic conductivity of soil (K u ) is one of the most principal parameters in the study of water movement in the soil. The field measurement methods of (K u ) are hard and expensive. So, indirect prediction of (K u ) has received considerable attention as published in the research papers to be an alternative approach. However, prediction models for soil hydraulic conductivity are now widely used informative tools for rapid and cost-effective assessment. Thus in this study, an attempt has been made to apply an adaptive neuro-fuzzy inference system (ANFIS) for predicting (K u ). The input variables were ECRatio (electric conductivity of water divided by electric conductivity of soil), SARRatio (sodium adsorption ratio of water divided by sodium adsorption ratio of soil), soil texture index (calculated from clay, sand and silt), suction rate, organic matter in the soil, initial soil moisture content and initial soil bulk density. The Gaussian membership function was the best for the input variables. The Hybrid learning was selected for predicting (K u ) with ANFIS. Three performance functions namely; root mean squared error (RMSE), mean error (ME) and coefficient of determination (R 2 ), were used to evaluate the predictive capability of the suggested (ANFIS). The obtained results for testing data (9 points) indicated that the R 2 values relating predicted versus measured estimates of (K u ) was 0.783, ME was found to be 0.118 cm/sec and RMSE was found to be 0.472 cm/sec. As a result, it appears that applying ANFIS suggests a new approach for determining (K u ) along with saving time and cost.

he objective of this study was to develop an interactive application using C-Sharplanguage to predict cumulative infiltration rate of water in a soil.Cumulative infiltration rate seems to be very simple issue, but field determination of it is very tedious and time consuming task due to many factors affectingit. Thus, researchers should be encouraged to develop a simple and accurate model to predict the cumulative infiltration. The actual measurements of infiltrationin this study were obtained using double ring infiltrometer.The measurements were conducted in the field on different soil textures namely sand, sandy loam, loam and loamy sand. Moreover, different water qualities were utilized. The inputs to the interactive application were soil electric conductivity, soil sodium adsorption ratio, percentage of organic matter in the soil,initial soil water contents, initial soil bulk density, sodium adsorption ratio of water, electric conductivity of water and an index to represent soil texture.The mean error between actual and predicted cumulative infiltration rate by the help of the developed interactive application after three hours was-39.25 mm. Consequently, the developed C-Sharp application is recommended for estimating cumulative infiltration rate in soilsto provide data for irrigation water management.