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您的检索式：作者名="Samad EMAMGHOLIZADEH"
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| 1 | Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)显示文摘Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients. | Samad EMAMGHOLIZADEH Shahin SHAHSAVANI Mohamad Amin ESLAMI | 2017 | Chinese Geographical Science2017，27，5： | 5 |
| 2 | Experimental study of the velocity of density currents in convergent and divergent channels显示文摘The head velocity of the density current in the convergent and divergent channel is a key parameter for evaluating the extent to which suspended material travels,and for determining the type and distribution of sediment in the water body.This study experimentally evaluated the effects of the reach degree of convergence and divergence on the head velocity of the density current.Experiments were conducted in the flume with 6.0 m long,0.72 m width and 0.6 m height.The head velocity was measured at three convergent degrees(-8°;-12°;-26°),at three divergent degrees(8°;12°;26°) and two slopes(0.009,0.016) for various discharges.The measured head velocity of the density current is compared with the head velocity of the density current in the constant cross section channel.Based on non-dimensional and statistical analysis,relations as linear multiple regression are offered for predicting head velocity of the density current in the convergent,divergent and constant cross section channel.Also the results of this research show that for the same slope and discharge,the head velocity of the density current in the convergent and divergent channel are greater and less than the head velocity of the constant cross section,respectively. | Hasan Torabi POUDEH Samad EMAMGHOLIZADEH Manoocher Fathi-MOGHADAM | 2014 | International Journal of Sediment Research2014，29，4： | 2 |
| 3 | Experimental study of the velocity of density currents in convergent and divergent channels显示文摘在会聚、分叉的隧道的密度水流的头速度是为评估到哪个推迟了材料旅行的程度,并且为在水身体决定沉积的类型和分发的一个关键参数。这研究试验性地在密度水流的头速度上评估了集中和分叉的活动范围度的效果。实验长与 6.0 m 在斜槽被进行, 0.72 m 宽度和 0.6 m 高度。 | Hasan Torabi POUDEH Samad EMAMGHOLIZADEH Manoocher Fathi-MOGHADAM | 2013 | International Journal of Sediment Research2013，28，2： | 0 |
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