A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting

Natural Resources Research - This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used...     

Health risk assessment of nitrate exposure in groundwater of rural areas of Gonabad and Bajestan,Iran

Prolonged exposure to excessive levels of nitrate through drinking water is a potential risk for human health. The current research reports the analytical results and associated health risk for water quality in term of nitrate in 39 groundwater samples during January 2018 in rural areas of Gonabad and Bajestan, Iran. Nitrate concentrations ranged from 1.8 to 82.2 and from 5.5 to 84.3 mg/L for Gonabad and Bajestan, respectively. In this work, the potential risk to human health was determined using the hazard quotient (HQ) for three age groups including adults, children and infants. Comparison of HQs among the 39 sampling sites showed that the rural areas in Bajestan had higher HQs than Gonabad. Among the studied groups, infants exposed to a higher risk than children and adults. The results also indicated that the health of individuals from nitrate exposure in most of the groundwater studied was not acceptable and most of the consumers were in danger from current nitrate concentrations. Therefore, there is an urgent need for enforcing effective plans to improve groundwater quality and to better manage and control probable nitrate contaminated sources.     

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

In this research, the simulation of Urmia Lake water level fluctuation by means of two models was applied. For this, Support Vector Machines (SVM), and Neural Wavelet Network (NWN) models that conjugated both the wavelet function and ANN, developed for simulating the Urmia Lake water level fluctuation. The yearly data of rainfall, temperature and discharge to the Urmia Lake and water level fluctuation were used. Urmia Lake is the biggest and the hyper saline lake in Iran. The outcome of the SVM based models are compared with the NWN. The results of SVM model performs better than NWN and offered a practical solution to the problem of water level fluctuation predictions. Analysis results showed that the optimal situation occurred with use of precipitation, temperature and discharge for all station and water level fluctuations at the lag time of one year (RMSEs) of 0.23, 0.41 m obtained by SVM, NWN, respectively, and SSEs of 0.43, 1.33 and R ² of 0.97, 0 obtained by SVM, NWN, respectively. The results of SVM model show better accuracy in comparison with the NWN model.     

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.     

Experimental Investigation of Rheological Behavior and Wax Deposition of Waxy Oil–Disulfide Oil Systems

Natural Resources Research - In this work, we studied the feasibility of using disulfide oil (DSO) as a solvent for wax prevention in pipelines. Several tests were carried out to determine the...     

Shallow/upper crustal shear wave structure of the Tehran region (Central Alborz,Iran) from the inversion of Rayleigh wave dispersion measurements

Surface wave dispersion curves from microearthquakes are used to obtain group velocity dispersion maps. The calculation of the local dispersion curves for each grid point from these maps then produces the input data to retrieve the 3D shear wave velocity model of the Tehran region. The group velocity maps indicate that the tomographic results agree well with the three main tectonic features and the geological units in the study area. The tomographic maps generally possess high-velocity structures across most of the mountain belts (Central Alborz and east-southeast mountains), whereas the Tehran Basin correlates to a low-velocity structure. Increasing the period in the study area highlights four independent low-velocity zones that reflect faults and fault junction systems. The shear wave velocity profiles indicate that the depth to bedrock exhibits southward variation ranging from ~?300 m to ~?1500 m. We also focus our analysis on the existence of faults within the shear wave profiles and discuss the low shear wave velocity anomalies deeper than 2 km result from the main fault structures (e.g., North Tehran, North-South Rey and Parchin). Furthermore, we argue that the dip angle of the North Tehran fault varies along fault strike, whereas the North-South Rey fault possesses a constant dip angle. Moreover, initial model uncertainties and checkerboard resolution tests are used to identify reliable and robust anomaly features in the 3D shear wave velocity model and 2D tomographic maps, respectively. Microearthquake analysis provides an effective approach for studying the upper crustal structure heterogeneity, especially the fault structure, of the Tehran region.     

Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach

Water Resources - This study proposed a hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble...     

Spatial analysis of groundwater quality for drinking purpose in Sirjan Plain,Iran by fuzzy logic in GIS

At present,due to shortage of water resources,especially in arid and semiarid areas of the world such as Iran,exploitation of groundwater resources with suitable quality for drinking is of high importance.In this regard,contamination of groundwater resources to heavy metals,especially arsenic,is one of the most important hazards that threaten human health.The present study aims to develop an approach for presenting the groundwater quality of Sirjan city in Kerman Province,based on modern tools of spatial zoning in the GIS environment and a fuzzy approach of evaluating drinking water in accordance with the standards of world health organization(WHO).For this purpose,qualitative data related to 22 exploitation wells recorded during 2002 to 2017 were used.In addition,fuzzy aggregate maps were prepared in two scenarios by neglecting and considering arsenic presence in groundwater resources.The results showed a decrease in groundwater quality over time.More specifically,neglecting the presence of arsenic,in 2002,all drinking wells in the area were located in an excellent zone,while in 2017 a number of operation wells were located in the good and medium zone.Also,the final map,considering the presence of arsenic as a limiting factor of drinking water,indicated that parts of the southern regions of the plain would be the best place to dig wells for drinking water.Therefore,the use of new methods can contribute significantly to the usage of groundwater aquifers and provide a good view of the aquifer water quality.     

The effect of soil–structure interaction on the seismic risk to buildings

This paper studies the effect of soil–structure interaction (SSI) on the seismic risk estimates of buildings. Risk, in this context, denotes the probability distribution of seismic monetary loss due to structural and nonstructural damage. The risk analysis here uncovers the probability that SSI is beneficial, detrimental, or uninfluential on seismic losses. The analyses are conducted for a wide range of buildings with different structural systems, numbers of stories, and foundation sizes on various soil types. A probabilistic approach is employed to account for prevailing sources of uncertainty, i.e., those in ground motion and in the properties of the soil–structure system. In this approach, probabilistic models are employed to predict the response, damage, and repair cost of buildings. To properly account for the ground motion uncertainty, a suite of nearly 7000 accelerograms recorded on soil is employed. It is concluded that structures on very soft soils are extremely likely to incur smaller losses due to SSI, which is in line with the common belief that SSI is a favorable effect for such systems. However, the results for buildings on moderately soft soils reveal a considerable probability, up to 0.4, that SSI has an adverse effect on the structure and increases the seismic losses.