Thamer Ahmed Mohamed

Flow and sediment transport in vegetated waterways: a review

One of the most important waterway components is vegetation, which play a pivotal role in the flow and sediment transport. Vegetation environment and characteristics, including vegetation porosity or density, shape, flexibility and vegetation height, are significantly affected in vegetated channels. Various vegetation positions and arrangements such as patches significantly affect the sediment deposition rate and flow turbulence. This paper reviews recent works conducted on vegetated open channels, which include the effect of different vegetation arrangements and vegetation characteristics on mass transport and turbulence structure. Studies based on laboratory, field works, and modeling, have been reviewed based on previous methods used by different researchers. Methods used in vegetation porosity evaluation, rate of flow and sediment transport properties are presented. In short, flow and transport depend on the vegetation properties and flow structure conditions.

Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping

Abstract This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r2) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs.

The development and applications of solar pond: a review

Solar energy is a substantial source of clean and renewable energy. Recently, solar pond technology has made a great progress due to the fact that solar ponds can collect and store heat in the same time which has reduced the cost significantly thus making it an area of interest for the industry and researchers. Several salt gradient solar ponds have been constructed in many regions around the world. This paper reviews the various schemes of solar ponds, in addition to their performance, optimization, heat extraction mode and applications as reported in the literature. The maintenance and cost effect on solar pond have also been discussed.

Thermal and structural analysis of RCC double-curvature arch dam

AbstractThis paper focuses on the development, verification and application of a three-dimensional finite element code for coupled thermal and structural analysis of roller compacted concrete arch dams. The Karun III Arch dam located on Karun River, Iran, which was originally designed as a conventional concrete arch dam, has been taken for the purpose of verification of the finite element code. In this study, RCC technology has been ascertained as an alternative method to reduce the cost of the project and make it competitive. A numerical procedure to simulate the construction process of RCC arch dams is presented. It takes into account the more relevant features of the behavior of concrete such as hydration, ageing and creep. A viscoelastic model, including ageing effects and thermal dependent properties is adopted for the concrete. The different isothermal temperature influence on creep and elastic modulus is taken into account by the maturity concept. Crack index is used to assess the occurrence of cra...

Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach

Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash – Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods.

An evaluation of existent methods for estimation of embankment dam breach parameters

The study of dam-break analysis is considered important to predict the peak discharge during dam failure. This is essential to assess economic, social and environmental impacts downstream and to prepare the emergency response plan. Dam breach parameters such as breach width, breach height and breach formation time are the key variables to estimate the peak discharge during dam break. This study presents the evaluation of existing methods for estimation of dam breach parameters. Since all of these methods adopt regression analysis, uncertainty analysis of these methods becomes necessary to assess their performance. Uncertainty was performed using the data of more than 140 case studies of past recorded failures of dams, collected from different sources in the literature. The accuracy of the existing methods was tested, and the values of mean absolute relative error were found to be ranging from 0.39 to 1.05 for dam breach width estimation and from 0.6 to 0.8 for dam failure time estimation. In this study, artificial neural network (ANN) was recommended as an alternate method for estimation of dam breach parameters. The ANN method is proposed due to its accurate prediction when it was applied to similar other cases in water resources.

An integrated technique using solar and evaporation ponds for effective brine disposal management

ABSTRACT Desalination is a process that involves the removal of salts and non-ionic minerals from seawater to produce freshwater that is fit for human consumption. This process produces brine, which is typically redisposed into the sea. The relatively high salt concentration in the disposed brine increases the salinity of water and soil, which adversely affects the environment. However, brine is found to be rich in economically valuable minerals. In order to effectively manage the disposed brine, this study proposes an integrated technique using solar and evaporation ponds to filter valuable minerals from concentrated brine. The results of this study demonstrate that the proposed technique can be effectively employed for this purpose. Furthermore, this helps reduce desalination costs and complies with the notion of renewable energy production and eco-friendliness.

Effects of depth-varying vegetation roughness in two-dimensional hydrodynamic modelling

ABSTRACT For detailed hydrodynamic modelling of vegetated floodplains, the ability to quantify vegetation is advantageous as vegetation significantly influences the flow mechanism. Although it is widely known that roughness changes with depths, many two-dimensional (2D) models assign constant or generic roughness and the values are typically adjusted for calibration. This practice is likely to lead to the misinterpretation of the flow mechanism. This paper assesses the effects of depth-varying vegetation roughness in 2D hydrodynamic modelling based on vegetation density derived from a remotely sensed regression analysis. The simulated flood extents, depths and velocities of a historical flood event were compared between the constant and depth-varying vegetation roughness coefficients and verified against historical data and literature. A minimum value of 0.03 was found for vegetation with the lowest density of 0.01 m−1 at 0.2 m depth and a maximum value of 0.20 for vegetation with the highest density of 0.20 m−1 at 2 m flow depth, resulting in the maximum differences in flood depths and velocities of 0.40 m and 0.25 m/s, respectively. This study presented a bridge between the theoretical and practical applications which can potentially be used for evaluating vegetation restoration and removal.

ARSENIC REMOVAL FROM AN EX-TIN MINING WATER POOL USING A MICROFILTRATION-COAGULATION PROCESS

The removal of arsenic from water by a microfiltration‐coagulation process was investigated. In general, arsenic removal was enhanced in the presence of a coagulant. Arsenic was removed (83% and 72%) from water containing 243–255μg/l arsenic, resulting in arsenic concentrations of 40μg/L and 68μg/L, using microfiltration‐coagulation and microfiltration, respectively. Furthermore, arsenic removal increased with decreasing specific flux, in which an almost linear relationships (r2 = 0.905) was found to relate the specific flux to arsenic removal during the microfiltration‐coagulation process. Coupling microfiltration with coagulation was also found to be effective in turbidity removal. Turbidity was decreased by 94% using microfiltration‐coagulation processes, while 70% turbidity removal was achieved using microfiltration.