Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production. In the present study<span style="...Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production. In the present study<span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> an attempt has been done on two phosphate deposits at Elsebaiya area on both sides of the River Nile namely</span><span style="font-family:Verdana;">:</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Um Tondoba mine at Elsebaiya East area and Western River Nile mine in Elsebaiya West area. Depending on the available data, statistical analysis illustrated differences in the distribution of P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and ore thickness within the studied areas. Geostatistics used to start with constructing variograms for P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and thickness for the two phosphate deposits to be used with ordinary kriging models, also constructing cross variograms between P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and thickness to be used with cokriging models where the difference in the variogram parameters reflected a specific variation for each deposit horizontally and vertically. The ordinary kriging models and cokriging models illustrated different distribution behavior through both the two kriging techniques.</span></span>展开更多
Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accura...Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.展开更多
The ash contents in coal particles were examined in the paper dependably on particle size and its density. So, the two-dimensional regressive function Z = Z（P, D） was the searched object, where Z is random variable ...The ash contents in coal particles were examined in the paper dependably on particle size and its density. So, the two-dimensional regressive function Z = Z（P, D） was the searched object, where Z is random variable describing ash contents, P---density and D---particle diameter. This dependence was determined based on experimental data concerning the coal of type 31. For this coal, the method of ordinary kriging was applied to calculate the values of random variable Z. This method required the proper selection of so-called variogram function, in which four forms were considered in this paper in purpose to select the best solution. The given results were then evaluated by the mean standard error value and compared with empirical data.展开更多
This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridg...This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.展开更多
文摘Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production. In the present study<span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> an attempt has been done on two phosphate deposits at Elsebaiya area on both sides of the River Nile namely</span><span style="font-family:Verdana;">:</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> Um Tondoba mine at Elsebaiya East area and Western River Nile mine in Elsebaiya West area. Depending on the available data, statistical analysis illustrated differences in the distribution of P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and ore thickness within the studied areas. Geostatistics used to start with constructing variograms for P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and thickness for the two phosphate deposits to be used with ordinary kriging models, also constructing cross variograms between P</span><sub><span style="font-size:12px;font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">O</span><sub><span style="font-size:12px;font-family:Verdana;">5</span></sub><span style="font-family:Verdana;"> % and thickness to be used with cokriging models where the difference in the variogram parameters reflected a specific variation for each deposit horizontally and vertically. The ordinary kriging models and cokriging models illustrated different distribution behavior through both the two kriging techniques.</span></span>
基金Under the auspices of the National Natural Science Foundation of China(No.41571217)the National Key Research and Development Program of China(No.2016YFD0300801)
文摘Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.
文摘The ash contents in coal particles were examined in the paper dependably on particle size and its density. So, the two-dimensional regressive function Z = Z（P, D） was the searched object, where Z is random variable describing ash contents, P---density and D---particle diameter. This dependence was determined based on experimental data concerning the coal of type 31. For this coal, the method of ordinary kriging was applied to calculate the values of random variable Z. This method required the proper selection of so-called variogram function, in which four forms were considered in this paper in purpose to select the best solution. The given results were then evaluated by the mean standard error value and compared with empirical data.
文摘This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.
