Estimation of suspended sediment concentration by acoustic equations for soil sediment

The acoustic backscattering systems, ABS, for sediment measurement are based on the determining of the backscattering and attenuation properties of the particles in suspension. The relevant acoustic quantities are the form function, f, which describes the backscattering characteristics, and thenormalized total scattering cross-section, , which describes the attenuating characteristics of the particles in suspension. Formulations are required for these parameters of suspension sediment particles with size and acoustic frequency. Several studies have been conducted to determine theconcentration of sediments such as glass spheres or sand. However, the acoustic properties of natural sediments vary and depend on many parameters such as particle size, shape, mineralogy and distribution of those parameters in sample. Therefore, this study was conducted to determine the possibility of soil sediment concentration with the f and equations, which were obtained for glass spheres and sandy sediments under laboratory and river conditions. The results show that theacoustic method, especially with glass scattering equation, works fairly well to calculate soil sediment for low concentration range at laboratory and river conditions

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better