Faridah Othman

Arsenic, Zinc, and Aluminium Removal from Gold Mine Wastewater Effluents and Accumulation by Submerged Aquatic Plants (Cabomba piauhyensis, Egeria densa, and Hydrilla verticillata)

The potential of three submerged aquatic plant species (Cabomba piauhyensis, Egeria densa, and Hydrilla verticillata) to be used for As, Al, and Zn phytoremediation was tested. The plants were exposed for 14 days under hydroponic conditions to mine waste water effluents in order to assess the suitability of the aquatic plants to remediate elevated multi-metals concentrations in mine waste water. The results show that the E. densa and H. verticillata are able to accumulate high amount of arsenic (95.2%) and zinc (93.7%) and resulted in a decrease of arsenic and zinc in the ambient water. On the other hand, C. piauhyensis shows remarkable aluminium accumulation in plant biomass (83.8%) compared to the other tested plants. The ability of these plants to accumulate the studied metals and survive throughout the experiment demonstrates the potential of these plants to remediate metal enriched water especially for mine drainage effluent. Among the three tested aquatic plants, H. verticillata was found to be the most applicable (84.5%) and suitable plant species to phytoremediate elevated metals and metalloid in mine related waste water.

Determination of the length of hydraulic jumps using artificial neural networks

Hydraulic jumps have many uses such as dissipation of energy while water is flowing over spillways, increasing the water surface channels for distribution, chlorinating of wastewater, and many other cases. The length of hydraulic jumps is one of the most important parameters in designing the stilling basin, however, it cannot be calculated by mathematical analyses only - experimental and laboratorial results should also be used. In this study, an artificial neural network (ANN) technique was developed to determine the length of the hydraulic jumps in a rectangular section with a horizontal apron. Two algorithms, namely Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rule back propagation (BP) are employed to reach optimum model. From the different model structures that were examined, an LM algorithm with a 3-4-1 structure was adapted as the final model. The selected model can predict the length of jumps with high accuracy and satisfy the evaluation criteria, with root mean square error RMSE=0.01224, mean absolute percentage error MAPE=2.59%, and coefficient of determination R^2=0.9962. A comparison between the selected ANN model and empirical Silvester equation was also done and the results showed that the ANN method is more precise.

Influence of Flow Shallowness on Scour Depth at Semi-Integral Bridge Piers

The paper presents the experimental study on the effect of flow shallowness on the scouring at semi-integral bridge piers. The experimental work was conducted in the Hydraulic Laboratory of University of Malaya. Out of 1020 data collected in the flow shallowness and time evolution experiments, 60 data were chosen at maximum scour depth to be analyzed. 30 data for sediment size of d50 = 0.8 mm and 30 data for d50 = 0.26 mm were taken at each side of the piers. The results showed that similar trend was produced for both sediment sizes, the depth of scour increased with the flow depth. Almost all of the data plotted gave the best correlation coefficient. The scour depth at the left and right side of the pier were almost averaged.

Developing Optimal Reservoir Operation for Multiple and Multipurpose Reservoirs Using Mathematical Programming

Over the last decades, the increasing water demand has caused a number of problems, to which reservoir operation optimization has been suggested as one of the best solutions. In this research, a model based on mixed integer linear programming (MILP) technique is developed for the systematic operation of multireservoirs that are used to cater for the different needs of the Tehran-Karaj plain. These reservoirs include Laar, Latian, and Karaj dams. The system configuration was accomplished through the nodes and arcs of the network flow model approach and system component implementation including sources, consumption, junctions, and the physical and hydraulic relationship between them. The following were performed via comprehensive developed software: system configuration, objective function and constraints formulation, linearization, determining penalty values, and setting priorities for each node and arc in the system. A comparison between the MILP developed model’s results against the periodic data shows 21.7% less overflow, 11.6% more outflow, and 15.9% more reservoir storage, respectively. The outcome of the MILP-based modeling indicates superior performance to the historical period.

Comparative SWOT Analysis for Water Solutions in Asia and Africa

Various scientific and technological based solutions such as virtual water trading, desalination, groundwater extraction and wastewater reuse have been proposed and implemented in many parts of Asia and Africa to relieve water scarcity. This paper applies SWOT (strengths, weaknesses, opportunities and threats) analysis to examine the suitability of these alternative water solutions to alleviate water scarcity. SWOT analysis proves to be a useful decision making tool as it provides a qualitative approach to simplify multilayer and interdisciplinary problems. The main input for the SWOT analysis consists of knowledge gathered from seven experts in the field of water and environmental sciences. The results show that compared to other solutions, wastewater reuse offers the most desirable qualities as a viable water solution for sustainable water management in Asia and Africa.

Runoff Potentiality of a Watershed through SCS and Functional Data Analysis Technique

Runoff potentiality of a watershed was assessed based on identifying curve number (CN), soil conservation service (SCS), and functional data analysis (FDA) techniques. Daily discrete rainfall data were collected from weather stations in the study area and analyzed through lowess method for smoothing curve. As runoff data represents a periodic pattern in each watershed, Fourier series was introduced to fit the smooth curve of eight watersheds. Seven terms of Fourier series were introduced for the watersheds 5 and 8, while 8 terms of Fourier series were used for the rest of the watersheds for the best fit of data. Bootstrapping smooth curve analysis reveals that watersheds 1, 2, 3, 6, 7, and 8 are with monthly mean runoffs of 29, 24, 22, 23, 26, and 27 mm, respectively, and these watersheds would likely contribute to surface runoff in the study area. The purpose of this study was to transform runoff data into a smooth curve for representing the surface runoff pattern and mean runoff of each watershed through statistical method. This study provides information of runoff potentiality of each watershed and also provides input data for hydrological modeling.

Runoff trend and potentiality in Melaka Tengah catchment of Malaysia using SCS-CN and statistical technique

AbstractThis study is focused to identify the surface runoff trends and potentiality of the five watersheds transforming the discrete runoff pattern to smooth patterns. Runoff potentiality was analyzed by Soil Conservation Service Curve Number (SCS-CN) technique. Considering Hydrologic Soil Group (HSG) and percentage of particular land use pattern, weighted CNs of five watersheds were found between 82 and 85. Monthly surface runoff trends were investigated by statistical autocorrelation, Mann-Kendall, Sen slope and lowess methods. According to the Mann-Kendall method, no statistical significant monotonic trends were found for all the watersheds. Smoothing curve analysis reveals that the monthly mean runoff is 30 mm, 34 mm, 39 mm, 28 mm and 37 mm and the percentage of runoff is 23%, 25%, 31%, 25% and 26% for the watersheds 1, 2, 3, 4 and 5, respectively. Degree of effect of several land use pattern with corresponding soil type was analyzed to assess the total runoff volume for contributing to the surface w...

Optimization of Multiple and Multipurpose Reservoir System Operations by Using Matrix Structure (Case Study: Karun and Dez Reservoir Dams)

Optimal operation of water resources in multiple and multipurpose reservoirs is very complicated. This is because of the number of dams, each dam’s location (Series and parallel), conflict in objectives and the stochastic nature of the inflow of water in the system. In this paper, performance optimization of the system of Karun and Dez reservoir dams have been studied and investigated with the purposes of hydroelectric energy generation and providing water demand in 6 dams. On the Karun River, 5 dams have been built in the series arrangements, and the Dez dam has been built parallel to those 5 dams. One of the main achievements in this research is the implementation of the structure of production of hydroelectric energy as a function of matrix in MATLAB software. The results show that the role of objective function structure for generating hydroelectric energy in weighting method algorithm is more important than water supply. Nonetheless by implementing ε- constraint method algorithm, we can both increase hydroelectric power generation and supply around 85% of agricultural and industrial demands.

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.

RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers

Lag time (Lt) reflects the speed at which a river basin responds to rainfall (RF) events and is influenced by many hydrological parameters such as RF and stream flow (SF). These two parameters are represented by four variables, namely peak RF intensity, previous 48-h rainfall, peak SF and previous 48-h SF. In fact, lag time is highly stochastic in nature and its relation with the mentioned four variables is highly nonlinear interrelationship. The main objective of this study is to develop a model to estimate the Lt between upstream and downstream stations in tropical humid rivers. The graphical hydrological approach (HGA) has been used to estimate the Lt based on 95 RF-SF and considered as the references value for the proposed model evaluation. Linear, non-linear and Radial Basis Function Neural Network (RBFNN) methods have been developed successfully for Selangor River basin. The results show that the RBFNN outperformed the linear and the non-linear model and could achieve correlation coefficient (r) between the observed Lt and predicted Lt equal to 0.979 while r for the linear and the non-linear model equal to 0.519 and 0.631, receptively. Furthermore, the RBFNN model could attain minimum root mean square error (RMSE) between the observed Lt and predicted Lt equal to 1.23 while RMSE for the linear and the non-linear model equal to 1.9 and 2.02, receptively. The proposed RBFNN model significantly abridges the estimation of Lt values and avoids the essential need for comprehensive description of all parameters affecting on its value.