Efficiency of standardized image processing in the fragmentation prediction in the case of Sungun open-pit mine

The paper proposes a standardized image-processing procedure with the use of sieve analysis results for calibration which is utilized to measure the size distribution of fragmentation at Sungun mine. Through this procedure, a number of 19 bench blasting in various levels have been initially selected as the target of the study for each, multiple photos were taken immediately after blast from suitable perspectives and locations of the muckpiles surfaces. The number of image sampling was chosen adequately high to achieve further reliability of the whole photography procedure. Then fragments of each muckpile were separately mixed by a loader, where another image sampling from these new muckpiles, bucket of loaders, and haulage trucks was performed. For the purpose of sieve analysis, seven sieves with the mesh sizes between 1.27 cm (0.5 in) and 25.4 cm (10 in) were designed, manufactured, and then installed at Sungun semi-industrial laboratory. Additionally, three mass samples of the mixed fragments were randomly chosen among the 19 muckpiles for sieving. During image analysis stage, “sieve shift” and “mass power” factors, required to obtain standardized size distribution, were precisely assigned when the results obtained by the image analysis software was in accordance with the sieving results. In order to validate the reliability of the image processing, a comparative analysis of the achieved results was made with the results of the original Kuz–Ram model [Cunningham (1983) The Kuz–Ram model for prediction of fragmentation from blasting. In: Proceedings of the first international symposium on rock fragmentation by blasting, Lulea, Sweden, pp 439–454]. Finally, the image-processing procedure was found to be more efficient, with results close-matched to the real results of the sieve analysis.     

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index（SPI） was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation（AMO）,Atlantic Meridional Mode（AMM）,the Bivariate ENSO Time series（BEST）,the East Central Tropical Pacific Surface Temperature（NINO 3.4）,the Central Tropical Pacific Surface Temperature（NINO 4）,the North Tropical Atlantic Index（NTA）,the Southern Oscillation Index（SOI）,and the Tropical Northern Atlantic Index（TNA）.These indices accounted for 81% of the variance in the Principal Components Analysis（PCA） method.The Atlantic surface temperature（SST：Atlantic） had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.     

Dynamics of the self-interacting chameleon cosmology

In this article we study the properties of the flat FRW chameleon cosmology in which the cosmic expansion of the Universe is affected by the chameleon field and dark energy. In particular, we perform a detailed examination of the model in the light of numerical analysis. The results illustrate that the interacting chameleon filed plays an important role in late time universe acceleration and phantom crossing.     

Dynamical stability in scalar-tensor cosmology

We study FRW cosmology for a double scalar-tensor theory of gravity where two scalar fields are nonminimally coupled to the geometry. In a framework to study stability and attractor solutions of the model in the phase space, we constrain the model parameters with the observational data. For an accelerating universe, the model behaves like quintom dark energy models and predicts a transition from quintessence era to phantom era.     

Rainfall-runoff modeling considering soil moisture accounting algorithm,case study: Karoon III River basin

In contrast to event based hydrologic models which reveal how a basin responds to an individual rainfall event, continuous ones synthesize hydrologic processes over a longer time period that includes both dry and wet conditions. With respect to the importance of infiltration method in Rainfall-Runoff (RR) modeling, the objective of this study was to assess HEC-HMS with Soil Moisture Accounting (SMA) infiltration algorithm, considering several components of hydrologic cycle such as canopy interception, surface depression, infiltration into the soil profile storage, percolation to the ground water aquifer and base flow caused by available soil storage vs. maximum saturated capacity of soil layer, to model daily flows of Karoon III basin (Iran). The model showed satisfied performance by accounting initial moisture condition by SMA model with Nash-Sutcliffe (NS) coefficient of 0.76 and 0.64 for calibration and verification. Sensitivity analysis showed that saturated hydraulic conductivity (K), Clark storage coefficient (R) and time of concentration (t _c) were the most effective parameters on the simulated Peak Over Thresholds (POT). Results from this study assist in improving model accuracy and ability to predict future conditions based upon basin characteristic change.     

Hydraulic conductivity prediction based on grain-size distribution using M5 model tree

The hydraulic conductivity, K_s, is one of the most important hydraulic properties which controls the water and solute movement into the soil. It is measured on soil specimens in the laboratory. On the other hand, sometimes it is obtained by tests carried out in the field by a number of researchers. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools have been used to evaluate the hydraulic conductivity. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees, have been used for predicting the hydraulic conductivity. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and represent understandable mathematical rules more clearly. In this paper, a new formula that includes some parameters is derived to estimate the hydraulic conductivity. To develop the new formulas, experimental data sets of hydraulic conductivity were used. A comparison is made between the estimated hydraulic conductivity by this new formula and formulas given by other’s researches.     

Optimal location of additional exploratory drillholes using afuzzy-artificial bee colony algorithm

In most research studies, the problem of locating additional drillhole is simplified, and the ore body is considered as a 2d object. In this study, location of additional drillholes are optimized by considering the third dimension of the ore body, the azimuth and the dip of additional drill holes. A new objective function is defined to address the effect of rock type in locating new drillholes. The optimization problem is solved using a novel fuzzy-artificial bee colony algorithm, called FABC. The parameters of the FABC algorithm is dynamically adjusted using a designed fuzzy inference system with three performance measures as inputs and two outputs. The comparison performance with state-of-the-art optimization algorithm, using a nonparametric hypothesis test, indicates higher performance of the FABC algorithm. The results indicate significantly a decrease of kriging variance by introducing additional drillholes.     

Watershed classification by remote sensing indices: A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean (FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups. The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently, the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.