Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

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.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

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.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

Effects of over-consolidation history on flow instability of clean and silty sands

Acta Geotechnica - This article presents an experimental program under the constant volume condition to investigate the influence of over-consolidation on flow instability of clean and silty sand...     

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.     

Prediction of Rock Abrasivity Index (RAI) and Uniaxial Compressive Strength (UCS) of Granite Building Stones Using Nondestructive Tests

Geotechnical and Geological Engineering - Rock abrasivity index (RAI) and uniaxial compressive strength (UCS) are two key parameters for assessing abrasivity and durability of building stones,...     

Impact of hydrogeomorphic processes on ecological functions of brown trout habits

In recent decades, due to river regulations and their impact on river morphology, brown trout populations have been declining along Lar River downstream of Lar Dam located near Tehran, Iran. Considering the recent water scarcity in the country, development of river habitat assessment models seems necessary. Therefore, in this research, an analytically applied approach is adopted to evaluate brown trout habitat by creating a relation among the hydrologic, hydraulic, geomorphic and ecologic processes. After field survey, dimensionless shear stress of the stream flow thresholds including environmental flow, bankfull flow, surface and subsurface sediment flow thresholds was calculated for Lar, Dalichay and Sefidab Rivers using Shields formula. Then, by considering the dimensionless shear stress ranges of the stream flow thresholds, functional flows ranges and duration were calculated together with ecological efficiency of the cross sections. In addition, effects of annual water yield and entrenchment ratio of the cross sections on habitat functionality were also worked out as a result of which an exponential interaction was developed between the dimensionless shear stress and discharge. Results show that an increase in functional flows ranges and duration, together with rising of ecological efficiency, is directly proportional to an increase in median bed sediment size, entrenchment ratio and annual water yield. Therefore, flow regime, cross-sectional geometry, water-surface slope and bed sediment size could be effective on the ecological functions of the brown trout’s life cycle and functionality of river flow.     

Performance analysis of Diesel engines fueled by biodiesel blends via thermodynamic simulation of an air-standard Diesel cycle

Because of environmental problems, it becomes necessary to develop alternative fuels that give engine performance at par with diesel. Among the alternative fuels, biodiesel and its blends hold good promises as an eco-friendly and the most promising alternative fuel for Diesel engine. The properties of biodiesel and its blends are found similar to that of diesel. Many researchers have experimentally evaluated the performance characteristics of conventional Diesel engines fueled by biodiesel and its blends. However, experiments require enormous effort, money and time. Hence, via finite-time thermodynamics simulation, an air-standard Diesel cycle model with heat transfer loss and variable specific heats of working fluid is analyzed to predict the performance of Diesel engine. The effect of compression ratio, cut-off ratio and fuel type on output work and thermal efficiency is investigated through the model. The fuels considered for the analysis are conventional diesel, rapeseed oil biodiesel and its blend (20 % biodiesel and 80 % diesel by volume). Numerical simulations showed that the output work and thermal efficiency of the engine decrease with increase of cut-off ratio for all fuels. Also, the model predicts similar performance with diesel and biodiesel blend which means that the biodiesel blend (20 % biodiesel and 80 % diesel by volume) could be a good alternative and eco-friendly fuel for conventional Diesel engines without any need to modify the engine.