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1.
Hamid Geranian Seyed Hassan Tabatabaei Hooshang H. Asadi Emmanuel John M. Carranza 《Natural Resources Research》2016,25(2):145-159
In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods. 相似文献
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Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure. 相似文献
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Hossein Basser Shahaboddin Shamshirband Dalibor Petković Hojat Karami Shatirah Akib Afshin Jahangirzadeh 《Natural Hazards》2014,73(3):1439-1449
This study proposes a new approach for determining the optimum dimensions of a protective spur dike to mitigate the amount of scour around existing spur dikes. Several parameters of a protective spur dike were studied to determine their optimum values, including length, angle, and distance. Also the effect of changes of flow intensity and sediment size were examined. The main objective of this article was to predict the optimum values of protective spur dikes to attain the best performance. To predict the parameters of protective spur dikes for controlling the scour around spur dikes, we used the adaptive neuro-fuzzy inference system method to construct a process that simulates the optimal parameters of a protective spur dike, including the actual length of the protective spur dike, the actual length of the main spur dikes, the distance between the protective spur dike and the first spur dike, the angle between the protective spur dike and the direction of flow, the intensity of the flow, and median size of the bed sediments. This intelligent estimator was implemented using MATLAB/Simulink, and the performances were investigated. The simulation results presented in this paper show the effectiveness of the developed method. 相似文献
5.
Comparing grain size distribution of sediment and original soil under raindrop detachment and raindrop-induced and flow transport mechanism 总被引：3，自引：0，他引：3
The grain size distribution (GSD) of sediment in comparison with the original soil GSD is discussed under different slopes (5, 15 and 25%) and rainfall intensities (30, 60 and 90 mm h–1 with respective duration of 30, 15 and 10 min) but identical runoff (15 mm). The sediment quantification was carried out by raindrop-induced flow transport (RIFT) or/and transport by flow (FT) using a rainfall simulator and a 6 × 1 m2 erosion plot and a silt loam. The results show a high degree of enrichment for size classes of 2–4 and 4–8 μm and a high degree of depletion for size classes of >63 μm under different slopes and rainfall intensities. In addition, the results show that the experimental enrichment ratio (ER) for particle size <16 μm under different slopes and rainfall intensities was greater than 1, while the ER for particle size >32 μm was less than 1. 相似文献
6.
Vahid?NouraniEmail authorView author&#;s OrcID profile Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562
Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds. 相似文献
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Rainfall-induced erosion involves the detachment of soil particles by raindrop impact and their transport by the combined action of the shallow surface runoff and raindrop impact.Although temporal variation in rainfall intensity(pattern)during natural rainstorms is a common phenomenon,the available information is inadequate to understand its effects on runoff and rainfall-induced erosion processes.To address this issue,four simulated rainfall patterns(constant,increasing,decreasing,and increasing-decreasing)with the same total kinetic energy were designed.Two soil types(sandy and sandy loam)were subjected to simulated rainfall using 15 cm×30 cm long detachment trays under infiltration conditions.For each simulation,runoff and sediment concentration were sampled at regular intervals.No obvious difference was observed in runoff across the two soil types,but there were significant differences in soil losses among the different rainfall patterns and stages.For varying-intensity rainfall patterns,the dominant sediment transport mechanism was not only influenced by raindrop detachment but also was affected by raindrop-induced shallow flow transport.Moreover,the efficiency of equations that predict the interrill erosion rate increased when the integrated raindrop impact and surface runoff rate were applied.Although the processes of interrill erosion are complex,the findings in this study may provide useful insight for developing models that predict the effects of rainfall pattern on runoff and erosion. 相似文献
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Mohammad Isazadeh Seyed Mostafa Biazar Afshin Ashrafzadeh 《Environmental Earth Sciences》2017,76(17):610
The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na+), and sulfate concentration (SO4 2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO4 2?, 1.05 mEq/l for Na+, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na+, K+, Ca2+, Mg2+, Cl?, SO4 2?, and HCO3 ?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs. 相似文献
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Moslem Fatehi Hooshang Asadi Haroni Amin Hossein Morshedy 《Arabian Journal of Geosciences》2017,10(22):487
Geostatistical optimization in designing infill boreholes is an important cost-effective approach in increasing the accuracy of the tonnage and grade of an ore deposit. In this research, a new approach is proposed to design the optimum infill directional boreholes. In the proposed approach, the Kriging estimation variance is considered as the objective function and the number and properties of the optimum boreholes are estimated to minimize the objective function. The optimization procedure is implemented by Particle Swarm Optimization (PSO) algorithm. Range of the spatial and directional properties of new boreholes is determined by considering the primary information of the mineralization and administrative constraint of drilling. Then, the PSO algorithm is iteratively applied, and in each iteration, the variation of the estimated Kriging variance after drilling the new boreholes is determined and properties of the new boreholes are updated. The iterative procedure of the algorithm is continued until minimum Kriging variance is satisfied. The approach was applied to the Dalli Cu-Au porphyry deposit in Iran and three new infill directional boreholes were designed by considering six earlier boreholes from the preliminary exploration stage. New optimum boreholes were located where less information from the preliminary exploration stage exists and the highest variance is considered. Two new boreholes are near to vertical (78°) and the third is an inclined with 55° dip. By drilling these three new boreholes, the estimated grade model could be upgraded by 20%. For simplicity, quickness and the ability to search for the required numbers and specifications of a group of directional boreholes in a 3D environment are the most advantages aspects of the proposed approach. 相似文献
10.
Seismic evaluation of rocking structures through performance assessment and fragility analysis 总被引：2，自引：2，他引：0
Numerical studies have been conducted for low- and medium-rise rocking structures to investigate their efficiency as earthquake-resisting systems in comparison with conventional structures. Several non-linear time-history analyses have been performed to evaluate seismic performance of selected cases at desired ground shaking levels, based on key parameters such as total and flexural story drifts and residual deformations. The Far-field record set is selected as input ground motions and median peak values of key parameters are taken as best estimates of system response. In addition, in order to evaluate the probability of exceeding relevant damage states, analytical fragility curves have been developed based on the results of the incremental dynamic analysis procedure. Small exceedance probabilities and acceptable margins against collapse, together with minor associated damages in main structural members, can be considered as superior seismic performance for medium-rise rocking systems. Low-rise rocking systems could provide significant performance improvement over their conventional counterparts notwithstanding certain weaknesses in their seismic response. 相似文献