A practical and efficient numerical scheme for the analysis of steady state unconfined seepage flows

The scaled boundary finite‐element method (SBFEM), a novel semi‐analytical technique, is applied to the analysis of the confined and unconfined seepage flow. This method combines the advantages of the finite‐element method and the boundary element method. In this method, only the boundary of the domain is discretized; no fundamental solution is required, and singularity problems can be modeled rigorously. Anisotropic and nonhomogeneous materials satisfying similarity are modeled without additional efforts. In this paper, SBFE equations and solution procedures for the analysis of seepage flow are outlined. The accuracy of the proposed method in modeling singularity problems is demonstrated by analyzing seepage flow under a concrete dam with a cutoff at heel. As only the boundary is discretized, the variable mesh technique is advisable for modeling unconfined seepage analyses. The accuracy, effectiveness, and efficiency of the method are demonstrated by modeling several unconfined seepage flow problems. Copyright © 2011 John Wiley & Sons, Ltd.     

Use of isotopic tracers to characterize the interaction of water components and nitrate contamination in the arid Rasafeh area (Syria)

Chemical and isotopic data in atmospheric precipitation, surface water, and groundwater in arid Rasafeh area, northeast Syria, are used to clarify the status of groundwater quality, the interaction of water components, groundwater dating, and vulnerability to anthropogenic contamination. Interpretation of chemical data with thermodynamic calculation reveals that the dissolution of evaporate mineral is the main factor of high salinity. The δ¹⁸O and δ²H relationships indicate that the groundwater is fed by mixing water from Euphrates River and precipitation and the isotope balance equation were used to estimate the contribution of the Euphrates River to the aquifers recharge. High tritium content, together with high ¹⁴C activity in the majority of groundwater samples, indicate shorter residence times and consequently potentially greater recharge. The presence of high nitrate concentration associated with high tritium concentration in both shallow and deep aquifer units indicates the presence of high permeability, so that groundwater is highly susceptible to anthropogenic contamination. Nitrate seems to derive exclusively from the application of N fertilizers. The high nitrate values are characteristic of the areas with intensive agricultural activity, indicating the importance of irrigated return flow on the groundwater.     

New Criterion for Lunar Crescent Visibility

A new criterion for lunar crescent visibility has been established using 737 observations, almost half of them obtained by the Islamic Crescent Observation Project (ICOP). This criterion is based on two variables, viz. the topocentric arc of vision and the topocentric crescent width. The new model is able to predict the visibility of the lunar crescent both for naked eye and optically aided observations. From the database we found a Danjon limit of 6.4 degrees. Vice-President of “Crescents, Calendars and Mawaqeet Committee” of AUASS.     

Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis

Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier.     

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and <Emphasis Type="Italic">t</Emphasis>-distributed deviations

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.     

A new empirical equation for predicting the maximum initial amplitude of submarine landslide-generated waves

Landslides - The accurate prediction of landslide tsunami amplitudes has been a challenging task given large uncertainties associated with landslide parameters and often the lack of enough...     

Uncertainty quantification of landslide runout motion considering soil interdependent anisotropy and fabric orientation

Landslides - Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent...     

A probabilistic framework to make a decision on the post-earthquake functionality of bridges considering the damage,residual displacement,and aftershock

Bulletin of Earthquake Engineering - Making a decision on the post-earthquake functionality of structures has always been one of the most challenging issues in earthquake engineering. There is an...     

Exploring disaster preparedness of students at university in Bangladesh

Natural Hazards - Adequate and effective disaster preparedness in each sector is indispensable to abate disaster impacts and intensify disaster resilience in Bangladesh for achieving sustainable...     

Reliability framework for characterizing heat wave and cold spell events

Natural Hazards - Extreme weather events such as heat waves and cold spells affect people’s lives. This study develops a probabilistic framework to evaluate heat waves and cold spells. As...