Theses and dissertations (Engineering and Built Environment)

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Estimation of suspended sediment yield flowing into Inanda Dam using genetic programming
(2016) Jaiyeola, Adesoji Tunbosun; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
Reservoirs are designed to specific volume called the dead storage to be able to withstand the quantity of particles in the rivers flowing into it during its design period called its economic life. Therefore, accurate calculation of the quantities of sediment being transported is of great significance in environment engineering, hydroelectric equipment longevity, river aesthetics, pollution and channel navigability. In this study different input combination of monthly upstream suspended sediment concentration and upstream flow dataset for Inanda Dam for 15 years was used to develop a model for each month of the year. The predictive abilities of each of the developed model to predict the quantity of suspended sediment flowing into Inanda Dam were also compared with those of the corresponding developed Sediment Rating Curves using two evaluation criteria - Determination of Coefficient (R2) and Root-Mean-Square Error (RMSE). The results from this study show that a genetic programming approach can be used to accurately predict the relationship between the streamflow and the suspended sediment load flowing into Inanda Dam. The twelve developed monthly genetic programming (GP) models produced a significantly low difference when the observed suspended sediment load was compared with the predicted suspended sediment load. The average R2 values and RMS error for the twelve developed models were 0.9996 and 0.3566 respectively during the validation phase. The Genetic Programming models were also able to replicate extreme hydrological events like predicting low and high suspended sediment load flowing into the dam. Moreover, the study also produced accurate sediment rating curve models with low RMSE values of between 0.3971 and 11.8852 and high R2 values of between 0.9833 and 0.9962. This shows that sediment rating curves can be used to predict historical missing data of the quantity of suspended sediment flowing into Inanda Dam using existing streamflow datasets. The results from this study further show that the predictions from the Genetic Programming models are better than the predictions from the Sediment Raring Curve models, especially in predicting large quantities of suspended sediment load during high streamflow such as during flood events. This proves that Genetic Programming technique is a better predictive tool than Sediment Raring Curve technique. In conclusion, the results from this study are very promising and support the use of Genetic Programming in predicting the nonlinear and complex relationship between suspended sediment load and streamflow at the inlet of Inanda Dam in KwaZulu-Natal. This will help planners and managers of the dam to understand the system better in terms of its problems and to find alternative ways to address them.
Hydrological modelling under limited data availability : a case study of Umdloti River, South Africa
(2016) Mashiyane, Thulasizwe Innocent; Adeyemo, Josiah
Due to the water scarcity in South Africa, new strategies in management planning are needed in order to sustain water resources. The increase of population and economic growth in South Africa has a negative effect on the water resources. Therefore, it should be well managed. The main concerns of the sustainability of water resources are hydropower, irrigation for agriculture, domestic and industries. Hence, the use of integrated water resources management in a single system which is built up by a river basin will help in water resources. This study was focused on water management issues: some of the principal causes of water shortages in UMdloti River are discussed. The current situation of water supply and demand at present is discussed. It also addressed some essential elements of reasonable, cooperative and sustainable water resources management solutions. Many developing countries are characterized as there is limited data availability, water scarcity and decrease of water levels in the dams. The eThekwini municipality is also having similar problems. Water resources have been modelled under this limited data using the hydrological modelling techniques by assessing the streamflow and observed data. The aim of the study was to address the issue of water management how water supply sources can be sustained to be manageable to meet the population growth demand considering the capacity of Hazelmere Dam demand downstream of the dam. Hydrological models, simulation, and decision making support systems were used to achieve all the research objectives. Hazelmere Dam has been modelled so that it can be used efficiently for the benefit of all users downstream of the dam for their economic and ecological benefits. Monthly reservoir inflow data for Hazelmere Dam was obtained from the Department of Water Affairs, South Africa. The nature of data is streamflow volume in mega liter (Ml) recorded for every month of the year. This was converted to mega cubic meter (Mm3) for use in the analysis herein. A period spanning 19 years of data (1994 – 2013) was used for the analysis. Six parametric probability distribution models were developed for estimating the monthly streamflow at Hazelmere Dam. These probability distribution functions include; Normal, Log-Normal (LN), Pearson III, Log-Pearson type III (LP3), Gumbel extreme value type1 (EVI) and Log-Gumbel (LG). It was observed that UMdloti River is smaller when compared with other rivers within the KwaZulu-Natal Province which could make it difficult to implement integrated water resources management. The hydro-meteorological data collected also has some limitations. The meteorological stations are far away to one another and this would make it difficult to attach their readings with the corresponding water basin. The comparison between the observed and simulated streamflow indicated that there was a good agreement between the observed and simulated discharge. Even though, the performance of the model was satisfactory, yet, it should not be generalized equally for all purposes. The erosion on the study area must be addressed by the stakeholders. It must be minimized in order to sustain the water resources of the UMdloti River. Erosion has a bad impact on the environment because it causes environmental degradation as well. Further investigations are recommended that account for the geological characteristics and the source of the base flow to make sure the rate of groundwater is sufficient for any future developments. Harnessing more energy from existing water sources within the frontier of the country is important in capacitating the South African Government’s commitment to reduction of the country’s greenhouse gas emissions and transition to a low-carbon economy while meeting a national target of 3,725 megawatts by 2030. This study also aimed to determine the amount of energy that can be generated from Hazelmere Dam on the uMdloti River, South Africa. Behavioral analyses of the Hazelmere reservoir were performed using plausible scenarios. Feasible alternative reservoir operation models were formulated and investigated to determine the best operating policy and power system configuration. This study determines the amounts of monthly and total annual energy that can be generated from Hazelmere reservoir based on turbines efficiencies of 75%, 85% and 90%. Optimization models were formulated to maximize hydropower generation within the constraints of existing abstractions, hydrological and system constraints. Differential evolution (DE) optimization method was adopted to resolve the optimization models. The methodology was applied for an operating season. The optimization models were formulated to maximize hydropower generation while keeping within the limits of existing irrigation demands. Differential evolution algorithm was employed to search feasible solution space for the best policy. Reservoir behavioural analysis was conducted to inspect the feasibility of generating hydropower from the Hazelmere reservoir under normal flow conditions. Optimization models were formulated to maximize hydropower generation from the dam. DE was employed to resolve the formulated models within the confines of the system constraints. It was found that 527.51 MWH of annual energy may be generated from the dam without system failure. Storage was maintained above critical levels while the reservoir supplied the full demands on the dam throughout the operating period indicating that the system yield is sufficient and there is no immediate need to augment the system.
Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa
(2014-06-13) Oyebode, Oluwaseun Kunle; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.
Non-revenue water : most suitable business model for water services authorities in South Africa : Ugu District Municipality
(2016) Mwelase, Lorraine Thulisile; Dzwairo, Bloodless; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
Water is a critical resource in Southern Africa. The region thus needs to protect both the quality and the quantity of its water resources through robust water conservation and demand management (WC/DM) measures. Water demand management encompasses activities that aim to decrease water demand, improve the efficiency of water use and prevent the deterioration of water resources. Water conservation refers to policies, measures or consumer practices that promote the conservation of water resources. Water resources should be used wisely to secure a water supply that is of good quality and enough for South Africa’s people and its natural environment, which provides the ecosystem that supports all forms of life. When a water utility systems experience water losses, the amount of water available to consumers is reduced, making it difficult to satisfy demand. Water losses also occur as a result of inaccuracies in customer meters, data errors in the billing system and unauthorised consumption. Such losses result in non-revenue water (NRW), which is a serious threat to the water supply sector. NRW refers to the water that is produced and lost without generating revenue for the utility. This research study investigated strategies that could be used to address the challenge of water losses, by developing a more suitable business model that could be incorporated into Ugu District Municipality (DM)’s existing NRW reduction strategies. The study was carried out in Amandawe and Umzinto zones of the District Municipality and it covered the period 01 March 2014 to June 2015. The study objectives were made up of four components. The first was to identify and prioritise the implementation of NRW reduction strategies. This was achieved by identifying the pipes to be closed off, which were supplying a significant number of consumers. For those pipes that were not closed off, flow meters were installed to measure the flow into and out of a zone. The system was then tested for zero pressure by isolating all closed valves to ensure that there were no potential feed-backs into the zone. Pressure gauges were set up on standpipes for routine pressure monitoring. The test was run at night (between 01.00 and 05.00 hours) when the system was under pressure. When the pressure dropped consistently, this meant that there was no feedback into a zone. Leaks were detected by logging the system in order to obtain night flows, which were analysed to determine the system behaviour. The results for Amandawe Zone after implementation of the pressure management programme, indicated that the average zone’s night pressure (AZNP) decreased from 7.38 bars to 5.95 bars. For Umzinto Zone, the AZNP dropped from 5.5 bars to 3.3 bars. The minimum night flows (MNFs) dropped from 34.80 m3/hr to 15.20 m3/hr in Amandawe Zone and from 6.4 m3/hr to 1.70 m3/hr in Umzinto Zone. The daily cost of excess night flow due to bursts was reduced from R2276.17/day to R862.61/day in Amandawe Zone and from R361.24/day to R40.46/day in Umzinto Zone, which provided huge savings. The second objective was to identify the sources and causes of water losses in the study area by conducting field measurements and observations. This was achieved by physically inspecting the infrastructure using visual observation, mechanical listening sticks, correlators, ground microphones and system loggers. The following indicators were used to physically identify underground leaks: unusually wet surfaces in landscaped areas, pools of water on the ground surface, noticeably green, soft and mouldy areas surrounded by drier surfaces, a notable drop in water pressure or flow volume, unexplained sudden increase in water demand or water use at a fairly steady rate for several billing cycles, cracks in paved surfaces, potholes or sink holes and the sudden appearance of dirty water in the main distribution system. For this study, the water losses in the system were found to be as a result of various causes including leaks, aging infrastructure, high pressure in the system, damaged pipes and illegal connections, among others. The third objective was to construct a water balance in order to determine the key performance indicators for the NRW reduction strategies. This was achieved by determining the system input volume (SIV), billed authorized consumption (BAC), unbilled metered consumption (UMC), unbilled unmetered consumption (UUC), real losses (RL), apparent losses (AL) and IWA Key Performance Indicators. Bulk and domestic meter readings were used to calculate the components of the water balance. The results of the water balance indicated that there was a decrease in the SIV from 904 kL/day to 523 kL/day in Amandawe Zone and from 382 kL/day to 221 kL/day in Umzinto Zone. The physical water losses were reduced from 611 kL/day to 377 kL/day in Amandawe Zone and from 93.8 kL/day to 45.8 kL/day in Umzinto Zone. The NRW was reduced from 659 kL/day to 395 kL/day in Amandawe Zone and from 94.2 kL/day to 46.2 kL/day in Umzinto Zone. The fourth objective was to develop the most suitable business model for Ugu DM based on the results arising from the first three objectives. Ugu DM needs to ensure both operational and financial efficiency. Operational efficiency could be achieved by minimising real water losses through reviewing water services standards, developing district metering areas, pressure management, leak detection and repair, reservoir control to stop overflows and pipe replacement programs. Financial efficiency could be achieved by carrying out regular meter testing and calibration, securing database integrity, managing illegal connections, ensuring that all customer connections have meters and ensuring that the tariff structures were cost reflective in order for the municipality to cover costs and generate revenue. Findings of this study could assist other water utilities operating under similar conditions. The implementation of this study’s results could have positive economic, social and environmental effects on Ugu DM. It was concluded that rezoning, pressure management and leak detection were the most critical NRW reduction strategies as they had a positive impact on the system. The main causes of leaks in the system were identified as aging infrastructure, high pressures in the system, and illegal connections. All the critical KPIs of IWA water balance responded positively after the implementation of the strategies by reducing. The operational and financial efficiencies were identified as critical for a WSA to develop a business model that could sustain itself.
Optimization of irrigation water in South Africa for sustainable and beneficial use
(2017) Ikudayisi, Akinola Mayowa; Adeyemo, Josiah
Water is an essential natural resource for human existence and survival on the earth. South Africa, a water stressed country, allocates a high percentage of its available consumptive water use to irrigation. Therefore, it is necessary that we optimize water use in order to enhance food security. This study presents the development of mathematical models for irrigation scheduling of crops, optimal irrigation water release and crop yields in Vaal Harts irrigation scheme (VIS) of South Africa. For efficient irrigation water management, an accurate estimation of reference evapotranspiration (ETₒ) should be carried out. However, due to non-availability of enough historical data for the study area, mathematical models were developed to estimate ETₒ. A 20-year monthly meteorological data was collected and analysed using two data–driven modeling techniques namely principal component analysis (PCA) and adaptive neuro-fuzzy inference systems (ANFIS). Furthermore, an artificial neural network (ANN) model was developed for real time prediction of future ETₒ for the study area. The real time irrigation scheduling of potatoes was developed using a crop growth simulation model called CROPWAT. It was used to determine the crop water productivity (CWP), which is a determinant of the relationship between water applied and crop yield. Finally, a new and novel evolutionary multi-objective optimization algorithm called combined Pareto multi-objective differential evolution (CPMDE) was applied to optimize irrigation water use and crop yield on the VIS farmland. The net irrigation benefit, land area and irrigation water use of maize, potatoes and groundnut were optimized. Results obtained show that ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity have less significance on the value of ETₒ. Also, ANN models with one hidden layer showed better predictive performance compared with other considered configurations. A 5-day time step irrigation schedule data and graphs showing the crop water requirements and irrigation water requirements was generated. This would enable farmers know when, where, and how much water to apply to a given farmland. Finally, the employed CPMDE optimization algorithm produced a set of non-dominated Pareto optimal solutions. The best solution suggests that maize, groundnut and potatoes should be planted on 403543.44 m2, 181542.00 m2 and 352876.05 m2areas of land respectively. This solution generates a total net benefit of ZAR 767,961.49, total planting area of 937961.49 m2 and irrigation water volume of 391,061.52 m3. Among the three crops optimized, maize has the greatest land area, followed by potatoes and groundnut. This shows that maize is more profitable than potatoes and groundnut with respect to crop yield and water use in the study area.
Real time optimal water allocation in the Orange River catchment in South Africa
(2015) Olofintoye, Oluwatosin Onaopemipo; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
The planning and management of water resources systems often involve formulation and establishment of optimal operating policies and the study of trade-off between different objectives. Due to the intricate nature of water resources management tasks, several models with varying degrees of complexities have been developed and applied for resolving water resources optimisation and allocation problems. Nevertheless, there still exist uncertainties about finding a generally consistent and trustworthy method that can find solutions which are very close to the global optimum in all scenarios. This study presents the development and application of a new evolutionary multi-objective optimisation algorithm, combined Pareto multi-objective differential evolution (CPMDE). The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel generational selection scheme. The new scheme provides a systematic approach for controlling elitism of the population which results in the simultaneous creation of short solution vectors that are suitable for local search and long vectors suitable for global search. By incorporating combined Pareto procedures, CPMDE is able to adaptively balance exploitation of non-dominated solutions found with exploration of the search space. Thus, it is able to escape all local optima and converge to the global Pareto-optimal front. The performance of CPMDE was compared with 14 state-of-the-art evolutionary multi-objective optimisation algorithms. A total of ten test problems and three real world problems were considered in the benchmark of the algorithm. Findings suggest that the new algorithm presents an improvement in convergence to global Pareto-optimal fronts especially on deceptive multi-modal functions where CPMDE clearly outperformed all other algorithms in convergence and diversity. The convergence metric on this problem was several orders of magnitude better than those of the other algorithms. Competitive results obtained from the benchmark of CPMDE suggest that it is a good alternative for solving real multi-objective optimisation problems. Also, values of a variance statistics further indicate that CPMDE is reliable and stable in finding solutions and converging to Pareto-optimal fronts in multi-objective optimisation problems. CPMDE was applied to resolve water allocation problems in the Orange River catchment in South Africa. Results obtained from the applications of CPMDE suggest it represents an improvement over some existing methods. CPMDE was applied to resolve water allocation problems in the agricultural and power sectors in South Africa. These sectors are strategic in forging economic growth, sustaining technological developments and contributing further to the overall development of the nation. They are also germane in capacitating the South African government’s commitment towards equity and poverty eradication and ensuring food security. Harnessing more hydropower from existing water sources within the frontier of the country is germane in capacitating the South African Government’s commitment to reduction of the countries’ greenhouse gas emissions and transition to a low-carbon economy while meeting a national target of 3 725 megawatts by 2030. Application of CPMDE algorithm in the behavioural analysis of the Vanderkloof reservoir showed an increase of 20 310 MWH in energy generation corresponding to a 3.2 percent increase. On analysis of storage trajectories over the operating period, it was found that the real time analysis incorporating a hybrid between CPMDE and ANN offers a procedure with a high ability to minimize deviation from target storage under the prevailing water stress condition. Overall, the real time analysis provides an improvement of 49.32 percent over the current practice. Further analysis involving starting the simulation with a proposed higher storage volume suggests that 728.53 GWH of annual energy may be generated from the reservoir under medium flow condition without system failure as opposed to 629 GWH produced from current practice. This corresponds to a 13.66 percent increase in energy generation. It was however noted that the water resources of the dam is not in excess. The water in the dam is just enough to meet all current demands. This calls for proper management policies for future operation of the reservoir to guard against excessive storage depletions. The study herein also involved the development of a decision support system for the daily operation of the Vanderkloof reservoir. This provides a low cost solution methodology suitable for the sustainable operation of the Vanderkloof dam in South Africa. Adopting real time optimisation strategies may be beneficial to the operation of reservoirs. Findings from the study herein indicate that the new algorithm represents an improvement over existing methods. Therefore, CPMDE presents a new tool that nations can adapt for the proper management of water resources towards the overall prosperity of their populace.