Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using GRACE data and hydrological models: A study from Turkey

Monitoring of the fluctuations of groundwater storage is particularly important in arid and semi-arid regions where water scarcity brings about various challenges. Remote sensing data and techniques play a preponderant role in developing solutions to environmental problems. The launch of Gravity Recovery and Climate Experiment (GRACE) satellites has eased the remote monitoring and evaluation of groundwater resources with an unprecedented precision over large scales. Within the scope of the current study, the latest release (RL06) of GRACE mass concentrations (Mascons) from Jet Propulsion Laboratory (JPL) dataset as well as Global Land Data Assimilation System (GLDAS) models of Noah and Catchment Land Surface Model (CLSM) were used to provide Groundwater Storage Anomalies (GWSA) over Turkey. The temporal interactions of the estimated GWSA with the climatic variables of precipitation and temperature (derived from the reanalysis datasets of CHELSA [Climatologies at High resolution for the Earth's Land Surface Areas] and FLDAS [the Famine Early Warning Systems Network Land Data Assimilation System], respectively) were investigated statistically. The results suggest that there is a descending trend (from 2003 to 2016) for Terrestrial Water Storage Anomalies (TWSA) and GWSA over Turkey with a total loss of 11 and 6 cm of water, respectively. The statistical analysis results also indicate that the monthly variations of GWSA over Turkey are highly correlated with precipitation and temperature at 2-month lag. The analysis of the climatology (long-term) values of monthly GWSA, precipitation and temperature also revealed high agreement between the variables.     

Investigation into the effect of fault properties on wave transmission

The estimation of wave transmission across the fractured rock masses is of great importance for rock engineers to assess the stability of rock slopes in open pit mines. Presence of fault, as a major discontinuity, in the jointed rock mass can significantly impact on the peak particle velocity and transmission of blast waves, particularly where a fault contains a thick infilling with weak mechanical properties. This paper aims to study the effect of fault properties on transmission of blasting waves using the distinct element method. First, a validation study was carried out on the wave transmission across a single joint and different rock mediums through undertaking a comparative study against analytical models. Then, the transmission of blast wave across a fault with thick infilling in the Golgohar iron mine, Iran, was numerically studied, and the results were compared with the field measurements. The blast wave was numerically simulated using a hybrid finite element and finite difference code which then the outcome was used as the input for the distinct element method analysis. The measured uplift of hanging wall, as a result of wave transmission across the fault, in the numerical model agrees well with the recorded field measurement. Finally, the validated numerical model was used to study the effect of fault properties on wave transmission. It was found that the fault inclination angle is the most effective parameter on the peak particle velocity and uplift. Copyright © 2017 John Wiley & Sons, Ltd.     

Investigation of Cycle Time Segments of Dragline Operation in Surface Coal Mine: A Statistical Approach

Dragline is highly capital intensive equipment to procure, operate and maintain in any surface mining operation. Given this, every second of operation of this capital intensive equipment is absolutely important. Improvement of even a single second in the total cycle time has a tremendous bearing on the overall performance of this equipment. In this light, the present paper is an endeavour to critically analyze the cycle time of dragline operations in a major surface coal mine in India. Rigorous statistical analysis has been performed on individual cycle time segments, of complete dragline cycle. The segmental cycle times have been found to be statistically significant and appear to be best represented by lognormal, normal and beta distributions. Furthermore, the mean time of the statistical distribution for segmental cycle time of dragline has revealed the dependence of cycle time on cut geometry and depth. Results have been illustrated in the form of figures, graphs and tables.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.     

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R² = 0.88; NS = 0.88; RMSE = 142.30 (m³/s); MAE = 88.94 (m³/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R² = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m³/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.     

Chemical identification,antioxidant, cholinesterase inhibitory,and cytotoxic properties of fucoidan extracted from Persian Gulf Sargassum angustifolium

Marine macroalgal sulfated fucose-containing polysaccharides, like fucoidan, have drawn significant attention due to their biotechnological potentials, such as anti-cancer, antioxidant, and anti-cholinesterase activities. The fucoidan derived from brown macroalgae Sargassum angustifolium species (FSA) was investigated for its cytotoxic effects and alterations in cell proliferation, and cell cycle-related gene expression in the present study occurred on NB4 cell line. The results showed that FSA would induce p53, p21, pro-apoptotic genes and increase expression of the p15 gene as a cell arrest marker. Also, FSA inhibited the anti-apoptotic effect of the Bcl-2 gene and decreased dnmt-1 gene expression. FSA significantly exhibited potent 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity (p<0.05) with an IC₅₀ value of 0.157 mg/mL and showed moderate anti-acetylcholinesterase activity with an IC₅₀ value of 1.20 μg/mL. These results indicated the potential of FSA for the development of therapeutic or preventive agents of cancer and Alzheimer’s disease mainly through cytotoxic effect and AChE (acetylcholinesterase) inhibition as well as additional antioxidant capacities.     

Modelling underground cadastral survey data in CityGML

In underground environments, survey elements such as survey points and observations provide the information required to define legal boundaries. These elements are also used to connect underground legal spaces to a geodetic survey network. Due to the issues of current 2D approaches for managing underground cadastral data, prominent 3D data models have been extended to support underground land administration. However, previous studies mostly focused on defining underground legal spaces and boundaries, with less emphasis on survey elements. This research aims to extend CityGML to support underground cadastral survey data. The proposed extension is based on the survey elements elicited from underground cadastral plans, which is then implemented for an underground case study area in Melbourne, Australia. This extension integrates underground survey data with legal and physical data in a 3D digital environment and provides an improved representation of survey elements, facilitating the management and communication of underground cadastral survey data.