An integrated GIS-based approach for geohazards risk assessment in coal mines

From the viewpoint of safety in underground coal mining, the most suitable mining panel is the one with minimum geological structures, the right machinery, and equipment selection, trained employee, and proficient stope management. Since the ground parameters are uncontrollable and inherent uncertainties exist, a high percent of risk will usually accompany the underground coal mining activities. The main purpose of this study is to present a geological–geotechnical risk assessment model for identification of high risk-prone areas in underground coal mines using an integrated GIS-geostatistics system. Tabas as the first mechanized and largest underground coal mine in Iran was selected as a case study in this study. Gas content of coal seam, Coal Mine Roof Rating (CMRR), initial in situ stress state, fault throw, and orientation were selected as hazard/risk factors. For estimating the amount of coal seam gas content, CMRR and initial in situ stress in unsampled areas and providing the prediction maps, geostatistics module in ArcGIS was used. Rock engineering system–interaction matrix method was used for attribute weight assignment. Next, the attribute layers were weighted, rated, and overlaid to create a final map of geohazards risk. The analysis results of final risk map indicate that about 45% of under study area is prone to high to very high geohazards risk. Comparison of the results with experiences obtained during the early part of the mine and mined-out panels showed generally good agreement with promising ideas. This highlights the potential application of the GIS-based approach for hazards detection and geohazards risk assessment in underground coal mines.     

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per- formance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91％ for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05％ using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.     

Geology,geochemistry, sulfur isotope composition,and fluid inclusion data of Farsesh barite deposit,Lorestan Province,Iran

Farsesh barite in the central part of Iranian Sanandaj-Sirjan zone is a sample of epigenetic hydrothermal mineralization in dolomitized limestone, which provides appropriate chemicophysical conditions making the passage of mineral-bearing fluids possible. Barite veins may range from a few centimeters to 2 m in thickness that increases downward. The microthermometry measurements obtained from more than 30 fluid inclusions show relative homogenization temperatures ranging from 125 to 200 °C with an average of 110 °C for Farsesh barite deposits. The mean salinity measured proves 16 times as much as weight percentage of NaCl for barite. Coexistence of liquid- and vapor-rich fluid inclusions in barite minerals may provide an evidence of boiling in ore veins. Moreover, occurrence of bladed calcite, high-grade ore zones, and presence of hydrothermal breccia are all consistent with boiling. Thermometric studies indicate that homogenization temperatures (Th) for primary and pseudosecondary fluid inclusions in barite range from 125 to 200 °C with an average of 1,100 °C. The δ³⁴S values of barite also lie between 8.88 and 16.6 %. The relatively narrow spread in δ³⁴S values may suggest uniform environmental conditions throughout the mineralization field. Thus, δ³⁴S values are lower than those of contemporaneous seawater, which indicates a contribution of magmatic sulfur to the ore-forming solution. Barite is marked by total amounts of rare Earth elements (REEs) (6.25–17.39 ppm). Moreover, chondrite-normalized REE patterns of barite indicate a fractionation of light REEs (i.e., LREEs) from La to Sm, similar to those for barite from different origins. The La_CN/Lu_CN ratios and chondrite-normalized REE patterns reveal that barite in Farsesh deposit is enriched in LREEs compared with heavy rare Earth elements (HREEs). Similarity between Ce/La ratios in barite samples and those found in deep-sea barite supports its marine origin. Lanthanum and Gd exhibit positive anomalies, which are common features of chemical marine sediments. Cerium shows a negative anomaly in most samples inherited from the negative Ce anomaly of hydrothermal fluid that is mixed with seawater at barite precipitation. The available data including tectonic setting, host rock characteristics, REE geochemistry, and sulfur isotopic compositions may support a hydrothermal submarine origin for Farsesh barite deposit.     

Compensatory growth response of rainbow trout Oncorhynchus mykiss Walbaum following short starvation periods

This sixty-day study was performed to determine the effects of short-term starvation and refeeding cycles on growth, feeding performances and body composition of rainbow trout(O ncorhynchus mykiss). Three hundred trout fingerlings with an average initial weight of 17.5±0.06 g were randomly distributed in 15 circular fiberglass tanks. The fish were exposed to 5 different feeding regimes; control: continuously fed twice daily to apparent satiation; T1 : starved for 1 day and re-fed for 2 days; T2 : starved for 1 day and re-fed for 4 days; T3 : starved for 3 days and re-fed for 12 days; T 4 : starved for 4 days and re-fed for 16 days. At the end of the experiment, growth performance, feed utilization, whole body ash and moisture contents were not significantly(P 0.05) different among the treatments. However, whole body protein content in T3 was significantly higher than other treatments(P 0.05). A significant difference in whole body fat content was observed between T3 and the control group at the end of the experiment(P 0.05). In conclusion this experiment suggests that feeding schedules involving starvation(1–4 days) and re-feeding cycles are a promising feed management tool for rainbow trout culture.     

Combining Group Method of Data Handling with Signal Processing Approaches to Improve Accuracy of Groundwater Level Modeling

Natural Resources Research - Groundwater level forecasting is a paramount necessity for integrated management of a basin. Development of suitable models is an essential step in determining...     

A technique for the analysis of the response of dams to earthquakes

A finite element technique is developed for two-dimensional problems of dynamics of dam–water–foundation systems taking into account all interactions rigorously. Water–foundation interaction, which previous developments have only simulated, is considered by imposing proper conditions at the fluid–solid interface. Furthermore, the technique permits treatment of layered foundations. An application to a concrete gravity dam–water–foundation system is presented and discussed.     

Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series

Stochastic Environmental Research and Risk Assessment - Modeling and forecasting the flow of rivers, especially in flood-prone areas using warning systems, enables officials to take the required...     

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir,Turkey

The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.     

Origin,toxicological and narcotic potential of sedimentary PAHs and remarkable even/odd n-alkane predominance in Bushehr Peninsula,the Persian Gulf

Surface sediments (n = 17) were collected from 6 sites located in the coasts of the Bushehr Peninsula. ∑₁₆PAH concentrations were in the range of 285.9–1288 ng g^? 1 dw and the sediments were moderately polluted. Py, Ph, B(a)A and Flu showed > 70% of the detected values in order of the mentioned rank. 4-ring PAHs were the dominant compounds and the molecular ratios presented the dominant pyrolytic source. The toxicity of PAHs for sediment dwelling life was averagely equivalent to occurrence of 0.08 ng g^? 1 of Dioxin in dw of the examined sediments. The evaluated narcotic potential (confidence level of 99%) was 0.027–3.04 and upper part of the Bushehr Peninsula, where the human population is more concentrated, showed more narcotic potential. ∑ n-alkane concentrations (n-C₁₅ ? n-C₃₂) were in the range of 3.10–35.6 μg g^? 1 dw. The dominant distribution of even carbon number n-alkanes was traced back to a possibly autochthonous source.