Issues in Eulerian–Lagrangian modeling of sediment transport under saltation regime

The saltation regime is very important for understanding the sediment transport mechanism. However,there is no consensus on a model for the saltation regime. This study answers several questions raised with respect to the Eulerian-Lagrangian modeling of sediment transport. The first question is why the previous saltation models that use different combinations of hydrodynamic forces yielded acceptable results? The second question is which shear lift model(i.e. a shear lift expression and its coefficient) is more appropriate? Another important question is which hydrodynamic forces have greater contributions to the saltation characteristics of a sediment particle? The last question is what are the contributions of the turbulence fluctuations as well as effects of using two-and three-dimensional(2 D and 3 D) models on the simulation results? In order to fairly answer these questions, a systematic study was done by considering different scenarios. The current study is the first attempt to clearly discuss these issues. A comprehensive 3 D saltation model for non-cohesive sediment was developed that includes all the hydrodynamic forces acting on the particle. The random nature of sediment transport was included using turbulent flow and bed-particle collision models. The eddy interaction model was applied to generate a3 D turbulent flow field. Bed-particle collisions were considered using the concept of a contact zone and a corresponding contact point. The validation of the model was done using the available experimental data for a wide range of sediment size(0.03 to 4.8 cm). For the first question, the results indicated that some of the hydrodynamic effects show opposing trends and some have negligible effects. With these opposing effects it is possible to adjust the coefficients of different models to achieve acceptable agreement with the same experimental data while omitting some aspects of the physics of the process. A suitable model for the shear lift force was developed by linking the lift coefficient to the drag coefficient and the contributions of the hydrodynamic forces and turbulence fluctuations as well as the consequences of using of 2 D and 3 D models were studied. The results indicate that the shear lift force and turbulent flow fluctuations are important factors for the saltation of both sand and gravel, and they cannot be ignored.     

Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.

     

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.     

Flood risk perception and its determinants among rural households in two communities in Khyber Pakhtunkhwa,Pakistan

Natural Hazards - Risk perception plays a vital part in flood risk management and mitigation strategies. Therefore, this study aims at first to measure the risk perception of the vulnerable...     

Separation of magnetic anomalies into induced and remanent magnetization contributions

Inversion of magnetic data is complicated by the presence of remanent magnetization, and it provides limited information about the magnetic source because of the insufficiency of data and constraint information. We propose a Fourier domain transformation allowing the separation of magnetic anomalies into the components caused by induced and remanent magnetizations. The approach is based on the hypothesis that each isolated source is homogeneous with a uniform and specific Koenigsberger ratio. The distributions of susceptibility and remanent magnetization are subsequently recovered from the separated anomalies. Anomaly components, susceptibility distribution and distribution of the remanent and total magnetization vectors (direction and intensity) can be achieved through the processing of the anomaly components. The proposed method therefore provides a procedure to test the hypotheses about target source and magnetic field, by verifying these models based on available information or a priori information from geology. We test our methods using synthetic and real data acquired over the Zhangfushan iron-ore deposit and the Yeshan polymetallic deposit in eastern China. All the tests yield favourable results and the obtained models are helpful for the geological interpretation.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

Detrital zircon provenance analysis in the Zagros Orogen,SW Iran: implications for the amalgamation history of the Neo-Tethys

International Journal of Earth Sciences - The Zagros Orogen developed as a result of Arabia–Eurasia collision. New in situ detrital zircon U–Pb and Hf isotopic analyses from a Cenozoic...     

Probabilistic seismic hazard assessment in the northeastern part of Algeria

This work involves updating the evaluation of seismic hazard in Northeast Algeria by a probabilistic approach. This reassessment attempts to resolve inconsistencies between seismic zoning in regional building codes and is further motivated by the need to refine the input data that are used to evaluate seismic hazard scenarios. We adopted a seismotectonic model that accounts for differences in interpretations of regional seismicity. We then performed a probabilistic assessment of regional seismic hazard in Northeast Algeria. Based on a homogeneous earthquake catalog and geological and seismotectonic data gathered in the first part of the study, a seismotectonic zoning map was created and seven risk areas were identified. For each area, peak ground acceleration hazard maps were produced. Details of the calculations are provided, including hazard curves at periods of 0.1, 0.2, 0.33, 0.5, 1.0, and 2.0 s and uniform hazard spectra at urban locations in the area, including Sétif, Constantine, Kherrata, Bejaia, and Jijel.     

Mapping thermal comfort in Iran based on geostatistical methods and bioclimatic indices

The cognition of thermal comfort plays a pivotal role in human life and activities. Recognizing thermal comfort based on climatic parameters is substantially significant. The main objective of the present study is to map thermal comfort using statistics from 43 meteorological stations, from 1970 to 2013. Initially, according to temperature and relative humidity, annual and seasonal thermal comfort conditions were mapped, and later bioclimatic human thermal comfort conditions in line with spatial factors were zoned based on bioclimatic indexes of Temperature Humidity Index (THI), effective temperature (ET) and Relative Strain Index (RSI). Among geostatistical methods, empirical Bayesian kriging (EBK) method with less RSME is more efficient. The annual distribution of temperature changes according to spatial factors of rugged topography and elevation, and latitude affects relative humidity. Thermal comfort in the northern and western half of Iran is higher than the southern and eastern areas of the country. Spatial factors of latitude and altitude reduce bioclimatic uniformity and create small areas with or without thermal comfort conditions. Bioclimatic indicators based on air temperature and relative humidity range of bioclimatic zones show. The results of ET and THI divide the whole country into six zones, from lack of thermal comfort to having thermal comfort conditions. Areas of southern strip as well as central and southeastern parts of the country do not have any human thermal comfort conditions in most of the year.     

Estimating geometrical parameters of cylindrical targets detected by ground-penetrating radar using template matching algorithm

In the current research, the ground-penetrating radar (GPR) method has been employed to identify physical and geometrical parameters of buried cylindrical structures using the pattern recognition approach. To achieve this goal, the well-established mathematical relationships between geometrical parameters of cylindrical target (radius, burial depth, and horizontal location) and the associated GPR hyperbolic response characteristics are employed using the template matching method. In order to validate the applicability of the template matching method in providing estimates of such parameters, the method is first examined on GPR responses of synthetic models with known geometrical parameters followed by applying on real data using two different similarity criteria including 2-D spatial convolution and normalized cross correlation in the wave number domain. In the first step, the GPR responses of 71 synthetic models encompassing one, two, and three horizontal cylinders were produced using the improved 2-D finite difference in frequency domain. Then, appropriate preprocessing sequences to reduce random noise caused by forward modeling were applied on synthetic data. The proposed algorithm applied on several synthetic model responses could estimate the known geometrical parameters of the buried cylinders with acceptable accuracy (maximum error of 15%). The template matching algorithm was also used to extract geometrical parameters of water and wastewater pipes buried in Imam Hossein Square, Isfahan city, as real GPR data. Depending on environmental conditions and subsurface host formation, the real GPR data normally contain a variety of noises; therefore, a series of appropriate objective preprocessing and processing stages were designed in order to apply on real GPR images before deploying template matching algorithm. The applicability of the template matching algorithm on real data and validity of the estimated parameters were proved based on assessing the accuracy of the estimated geometrical parameters of respective pipes through GPR response versus the measured parameters. The proposed algorithm was designed in such a way that all steps of estimating geometrical parameters of buried cylindrical targets are automatically carried out.