JWSS - Journal of Water and Soil Science

CEC of the soil is the exchange sites of organic and inorganic soil colloids. Modeling and Estimation of CEC is a useful indicator for fertility. The new alternative approaches for estimating CEC are indirect methods based on intelligent models. In this research in order to estimate CEC, 485 soil samples were prepared from two regions, chaparsar (Mazandaran in northern Iran) and Bostanabad (North of West Azarbaijan, Iran).In this paper introduces the application of genetic programming. Input parameters that are percent Clay, Organic Carbon and Silt, evaluate using genetic programming, neural network and Neural Inference Systems-Fuzzy models. The results indicate a good ability to intelligent models for CEC Estimation According to indices used in this study. Genetic programming model with a root mean square error of 1.78 and coefficient of determination 0.95 compared to other models have been more efficient and is able to provide satisfactory results, Also are the explicit solutions that reflect the relationship between input an output variable, was presented based on genetic programming. This preferred the genetic programming model adds the other models. Stepwise regression analysis to determine the contribution of each of the parameters indicated in the CEC that organic materials having Most coefficient of variation of 84% is justified CEC and clay and silt, respectively, with a correlation coefficient of 10% and 6% respectively.

Information on suspended sediment variation in times of flood is important in management of water resources, particularly management of basins, and in investigation of the causes of erosion. The relationship between discharge and suspended sediment concentration during floods is not similar and homogeneous for different reasons such as precipitation variety, discharge rate and sources of sediment and production of hysteretic loops. In this study, the instantaneous values of suspended load were simulated using genetic programming and regression methods. By comparing the two models, Genetic programming model was selected as the better one with the mean square error and determination coefficient of 0.8 and 0.5, respectively. Then based on this model, loops of suspended hysteretic load were drawn for the six events recorded in the period of 1387-1383. This resulted in 4 linear and 2 clockwise hysteretic loops for the river suspended sediment. Identifying various hysteretic loops is effective in determination of relative contributions of processes to production and transfer of sediment including amount and intensity of precipitation, flow rate and previous moisture conditions of watershed. The results showed that the clockwise hysteretic loops occurred usually in high precipitation and discharge, and linear hysteretic loops in spring because of low intensity precipitation.

Selection of optimum interpolation technique to estimate water quality parameters in unmeasured points plays an important role in managing the quality and quantity of water resources. The aim of this study is to evaluate the accuracy of interpolation methods using GIS and artificial neural network (ANNs) model. To this end, a series of qualitative parameters of samples from water taken from Dehgolan aquifer located in Kurdistan, Iran including CL, EC and PH were evaluated by any of the models. In this study, qualitative data from 56 observation wells with good dispersion in the whole plain was used. The data of 46 observation wells were used for calibration and the data of other 10 wells were used for verification of models. The results showed ANNs, IDW, and Kriging excellence and accuracy over other models in estimation of quality parameters CL, PH and EC. However the ANNs model is more accurate than other models. In case of lack of time and the need for acceptable accuracy and less risk in the estimation of qualitative parameters, the use of ANNs model is superior to other statistical models used.