New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...     

Estimation of concentration and exposure doses due to radon by using CR-39 plastic track detectors in the residences of Sudhnuti, Azad Kashmir, Pakistan

This paper presents the results of indoor radon concentration measurements in 120 dwellings of district Sudhnuti of Azad Kashmir. Measurements were taken with CR-39 passive alpha track detector. CR-39 based box type radon detectors were installed in a bedroom and living rooms of each house. The detectors were retrieved after exposing to indoor radon for period of 6 months and then etched in 6 M NaOH at 80°C for 16 h, the observed track densities were converted in to the indoor radon concentration. Indoor radon concentration varied from 20 ± 12 to 170 ± 4 Bq m⁻³ for the houses of the district Sudhnuti. Arithmetic mean (AM), geometric mean (GM) and geometric standard deviations (GSD) were found to be 82 ± 6, 77 ± 6 and 1.51, respectively. The minimum value of weighted average radon concentration was recorded in one of the house of Mang town, whereas the maximum value was found in the Pattan Sher Khan region. Doses due to indoor radon exposure vary from 0.50 ± 0.31 to 4.28 ± 0.11 mSv year⁻¹ AM, GM and GSD. of mean effective doses were found to be 2.06 ± 0.13, 1.95 ± 0.18 and 1.51, respectively. According to the recommendations made by the Health Protection Agency, UK (200 Bq m⁻³) all the houses surveyed are within the safe limits.     

Probabilistic rainfall thresholds for flood forecasting: evaluating different methodologies for modelling rainfall spatial correlation (or dependence)

Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub‐watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario. Copyright © 2011 John Wiley & Sons, Ltd.     

Determination of Realistic and Statistical Value of the Information Gathered from Exploratory Drilling

Unlike other branches of geosciences, exploratory drilling has not been investigated within the framework of an information system; so, the expression “value of exploratory drilling information” (despite its common usage) is vague. This article presents a model for the evaluation of value of the information gathered from exploratory drilling after studying different mineral exploration and exploratory drilling systems within the framework of an “information system.” Although this model does not present the economic value of information, it is a suitable tool for comparing different drilling patterns. The model was verified on the basis of drilling data for the Gol-Gohar XIIA anomaly.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.     

Sorption of lead by chemically modified rice bran

In this work, the effectiveness of native and chemically modified rice bran to remove heavy metal Pb(II) ions from aqueous solution was examined. Chemical modifications with some simple and low-cost chemicals resulted in enhancement of the adsorption capacities and had faster kinetics than native rice bran. Experiments were conducted in shake flasks to monitor the upshot of parameters over a range of pH, initial Pb(II) concentrations and contact times using a batch model study. The sorption capacities q (mg g^?1) increased in the following order: NaOH (147.78), Ca(OH)₂ (139.08), Al(OH)₃ (127.24), esterification (124.28), NaHCO₃ (118.08), methylation (118.88), Na₂CO₃ (117.12) and native (80.24). The utmost uptake capacity q (mg g^?1) was shown by NaOH-pretreated rice bran. The results showed that, using NaOH-modified rice bran, the chief removal of Pb(II) was 74.54 % at pH 5, primary Pb(II) concentration 100 mg L^?1 and contact time 240 min. Equilibrium isotherms for the Pb(II) adsorption were analyzed by Langmuir and Freundlich isotherm models. The Langmuir isotherm model, showing Pb(II) sorption as accessible through the high value of the correlation coefficient (R ² = 0.993), showed a q _max value of 416.61 mg g^?1. The kinetic model illustrated adsorption rates well, depicted by a second order, which gives an indication concerning the rate-limiting step. Thermodynamic evaluation of the metal ion ?G ^o was carried out and led to the observation that the adsorption reaction is spontaneous and endothermic in nature. NaOH chemically modified rice bran was a superb biosorbent for exclusion of Pb(II) and proved to be excellent for industrial applications.     

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.     

Evaluation of Tailings from a Porphyry Copper Mine based on Joint Simulation of Contaminants

Natural Resources Research - Tailings from porphyry copper mines contain environmentally harmful amounts of elements such as copper, molybdenum, lead and cobalt. Geostatistical simulation of...     

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions,a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.