The bituminous mixtures of Tall-e Abu Chizan: A Vth millennium BC settlement in southwestern Iran

Various bituminous artifacts were excavated from the Tall-e Abu Chizan, a late prehistoric (Middle Susiana to Middle Uruk) settlement on the middle of the Curvy plain, between the Karun River and the Ram Hormoz Plain. All samples dated from the Vth millennium BC and cover three periods: 5000–4700 BC (Late Middle Susiana), 4700–4200 BC (Late Susiana 1) and 4200–3900 BC (Late Susiana 2). The bitumens were studied using the techniques of petroleum geochemistry and were compared both to the unaltered crude oils produced from the main oil fields in the area and to the famous Mamatain oil seeps. All samples are very rich in bitumen (average 46 wt%) which has been biodegraded and oxidized. Despite these alteration phenomena, δ¹³C of asphaltenes occur within a narrow range of less than 1‰ PDB. Biodegradation affected the steranes, terpanes, dibenzothiophenes and mono- and triaromatic steroids. Molecular characteristics of terpanes, especially the occurrence of 18α (H)-oleanane, suggest that the bitumen from Tall-e Abu Chizan is a mixture generated from Cretaceous Kazdhumi and Eocene Pabdeh petroleum source rocks. In that respect, bitumens from Tall-e Abu Chizan belong to the same oil family as oil from the Naft Safid field, which is in the vicinity of the archaeological site. In fact, the bitumen at Tall-e Abu Chizan likely originated from oil seepages at Naft Safid. These oil seeps have not yet been sampled or analysed.     

Evidence for the occurrence of hydrogeochemical processes in the groundwater of Khoy plain,northwestern Iran,using ionic ratios and geochemical modeling

Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO₃, SO_4, and Cl), minor ions (NO₃ and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.     

Travel time computations using a compact eikonal equation for vertical transverse isotropic media

Eikonal solvers often have stability problems if the velocity model is mildly heterogeneous. We derive a stable and compact form of the eikonal equation for P‐wave propagation in vertical transverse isotropic media. The obtained formulation is more compact than other formulations and therefore computationally attractive. We implemented ray shooting for this new equation through a Hamiltonian formalism. Ray tracing based on this new equation is tested on both simple as well as more realistic mildly heterogeneous velocity models. We show through examples that the new equation gives travel times that coincide with the travel time picks from wave equation modelling for anisotropic wave propagation.     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

Improving sustainability of urban drainage systems for climate change adaptation using best management practices: a case study of Tehran,Iran

Although the effectiveness of best management practices (BMPs) in reducing urban flooding is widely recognized, the improved sustainability achieved by implementing BMPs in upstream suburban areas, reducing downstream urban floods, is still debated. This study introduces a new definition of urban drainage system (UDS) sustainability, focusing on BMP usage to enhance system performance after adaptation to climate change. Three types of hydraulic reliability index (HRI) plus robustness and improvability indices were used to quantify the potential enhanced sustainability of the system in a changing climate, together with a climate change adaptability index (CCAI). The sustainability of UDS for the safe conveyance of storm-water runoff was investigated under different land-use scenarios: No BMP, BMP in urban areas, and BMP inside and upstream of urban areas, considering climate change impacts. Rainfall–runoff simulation alongside drainage network modelling was conducted using a storm-water management model (US EPA SWMM) to determine the inundation areas for both base-line and future climatic conditions. A new method for disaggregating daily rainfall to hourly, proposed to provide a finer resolution of input rainfall to SWMM, was applied to a semi-urbanized catchment whose upstream runoff from mountainous areas may contribute to the storm-water runoff in downstream urban parts. Our findings confirm an increase in the number of inundation points and reduction in sustainability indices of UDS due to climate change. The results present an increase in UDS reliability from 4% to 16% and improvements in other sustainability indicators using BMPs in upstream suburban areas compared to implementing them in urban areas.     

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.     

Extraction of forward-directivity velocity pulses using S-Transform-based signal decomposition technique

Robust methods for time-frequency analysis of time series, which provide local information of signals, allow earthquake engineers to study both the input and output of dynamic time history analysis with more reliability. Moreover, time-frequency representations (TFRs) have a major role in the analysis of non-stationary seismic signals exhibiting significant time variation of frequency content. S-Transform (ST) is a modern TFR, which can measure local characteristics of a signal such as amplitude, frequency, and phase at any time instant. This paper presents a new method for decomposition of ground motion signals. A modified version of ST-based technique, originally employed to decompose signals of gearbox vibration, is introduced and applied to the extraction and characterization of pulse-like part of near-fault velocity records, which is contributed to the directivity effects. In addition, a new definition based on ST analysis is used to identify pulse period. The results of implementation of proposed procedure on a database of pulse-like ground motion recordings belonging to the different ranges of magnitude demonstrate the efficiency of proposed method compared with other available approaches. The results, also, indicate that simple approximation of distinct pulses using single-period waveforms, unlike the extracted pulses, cannot represent the impulsive nature of real records adequately.     

Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River,Iran

The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels of northwest Iran’s Aji-Chay River was assessed. The models were calibrated, validated and tested using different subsets of monthly records (October 1983 to September 2011) of individual solute (Ca²⁺, Mg²⁺, Na⁺, SO₄ ^2? and Cl^?) concentrations (input parameters, meq L^?1), and electrical conductivity-based salinity levels (output parameter, µS cm^?1), collected by the East Azarbaijan regional water authority. Based on the statistical criteria of coefficient of determination (R²), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (NSC) and threshold statistics (TS) the ANFIS model was found to outperform the ANN model. To develop coupled wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies, Symlet or Haar mother wavelets of different lengths (order), each implemented at three levels. To predict salinity input parameter series were used as input variables in different wavelet order/level-AI model combinations. Hybrid wavelet-ANFIS (R² = 0.9967, NRMSE = 2.9 × 10^?5 and NSC = 0.9951) and wavelet-ANN (R² = 0.996, NRMSE = 3.77 × 10^?5 and NSC = 0.9946) models implementing the db4 mother wavelet decomposition outperformed the ANFIS (R² = 0.9954, NRMSE = 3.77 × 10^?5 and NSC = 0.9914) and ANN (R² = 0.9936, NRMSE = 3.99 × 10^?5 and NSC = 0.9903) models.     

Hydrogeologic framework of the Maku area basalts, northwestern Iran

The Maku area in northwestern Iran is characterized by young lava flows which erupted from Mount Ararat in Turkey. These fractured volcanic rocks overlie alluvium associated with pre-existing rivers and form a good basalt-alluvium aquifer over an area of 650 km². Groundwater discharge occurs from 12 large springs, ranging from 20 to 4,000 L s^?1, and from some extraction wells. Permian and Oligo-Miocene age limestones along the northern boundary of the Bazargan and Poldasht Plains basalts are intensively karstified and groundwater from these high lands easily enters the basalt-alluvium aquifers. The transmissivity of the basalt-alluvium aquifer ranges from 24 to 870 m² d^?1, indicating heterogeneity. Groundwater of the aquifer is a sodium-bicarbonate and mixed cation-bicarbonate type and the concentration of fluoride is higher than the universal maximum admissible concentrations for drinking. In order to determine the chemical composition and identify the source of the high fluoride concentrations in the groundwater of the basaltic area, water samples from the springs, wells and rivers were analyzed. The results indicate that the high fluoride water enters the study area from the Sari Su River.     

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).