Regional distribution pattern of groundwater heavy metals resulting from agricultural activities

Contaminations of groundwater by heavy metals due to agricultural activities are growing recently. The objective of this study was to evaluate and map regional patterns of heavy metals (Cd, Zn and Cu) in groundwater on a plain with high agricultural activities. The study was conducted to investigate the concentration of heavy metals and distribution in groundwater in regions of Shush Danial and Andimeshk aquifers in the southern part of Iran. Presently, groundwater is the only appropriate and widely used source of drinking water for rural and urban communities in this region. The region covers an area of 1,100 km² between the Dez and Karkhe rivers, which lead to the Persian Gulf. For this study, the region was divided into four sub-regions A, B, C and D. Additionally, 168 groundwater samples were collected from 42 water wells during the earlier months of 2004. The flame atomic absorption spectrometry (AAS-Flame) was used to measure the concentration of heavy metals in water samples and the Surfer software was used for determination of the contour map of metal distribution. The results demonstrated that in all of the samples, Cd and Zn concentrations were below the EPA MCLG and EPA secondary standard, respectively. However, the Cu contents of 4.8 % of all samples were higher than EPA MCL. It is also indicated that the concentrations of metals were more pronounced at the southern part of the studied region than at the others. The analysis of fertilizers applied for agricultural activities at this region also indicated that a great majority of the above-mentioned heavy metals were discharged into the environment. Absence of confining layers, proximity to land surface, excess agricultural activities in the southern part and groundwater flow direction that is generally from the north to the southern parts in this area make the southern region of the Shush plain especially vulnerable to pollution by heavy metals than by other contaminants.     

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

Acta Geotechnica - The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such...     

Integration of Sequence Stratigraphic Analysis and 3D Geostatistical Modeling of Pliocene–Pleistocene Delta,F3 Block,Netherlands

This research is focused on the analysis of the sequence stratigraphic units of F3 Block, within a wave-dominated delta of Plio–Pleistocene age. Three wells of F3 block and a 3D seismic data, are utilized in this research. The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section. Integration of seismic sequence stratigraphic interpretation, using well logs, and subsequent 3D geostatistical modeling, using seismic data, aided to evaluat...     

Effects of non-submerged boulder on flow characteristics – A field investigation

The effect of fully submerged boulders on the flow structure in channels has been studied by some researchers. However, many natural streams have bed material with boulders that are not fully submerged under water. In many natural streams, boulders cover between 1% and 10% of the area of the stream reach. The effect of non-submerged boulders on the velocity profile and flow characteristics is very important for assessing riverbed deformation. The objectives of this paper are to find the pattern of velocity distribution around a non-submerged boulder and to compare it with the classical studies on flow resistance and Reynolds stress distribution in open channels. Also, by considering the variation in the Reynolds stress distribution at different locations around a non-submerged boulder, the effect of a non-submerged boulder on the estimation of shear velocity and resistance to flow has been investigated. Results indicates that inside the scour hole caused by a non-submerged boulder in a river velocity distributions are irregular. However, velocity distributions are regular outside the scour hole. The presence of the boulder causes a considerable deviation of the Reynolds shear stress from the classic distribution, showing a non-specific distribution with negative values. The classical methods for calculating shear velocity are not suitable because these methods do not give detailed velocity and Reynolds stress distributions in natural rivers with a lot of boulders. Thus, the effect of a non-submerged boulder on the estimation of the resistance to flow by considering the variations in velocity and Reynolds stress distributions at different locations around a non-submerged boulder is important and needs to be studied in a natural river instead of just in laboratory flumes. The negative values in Reynolds stress distribution around a boulder indicate that the classical methods are unable to predict resistance to flow, and also show strong turbulence inside the scour hole where the complex flow conditions present ambiguous Reynolds stress distributions. In the current study, to obtain a reasonable estimation of parameters in natural rivers, the classical method has been modified by considering velocity and Reynolds stress distributions through the boundary layer method.