Abstract: The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
Abstract: This study focuses on the hydro-geology and chemical
constituents analysis of Ikogosi Warm Spring waters in South West
Nigeria. Ikogosi warm spring is a global tourist attraction because it
has both warm and cold spring sources. Water samples from the cold
spring, warm spring and the meeting point were collected, analyzed
and the result shows close similarity in temperature, hydrogen iron
concentration (pH), alkalinity, hardness, Calcium, Magnesium,
Sodium, Iron, total dissolved solid and heavy metals. The measured
parameters in the water samples are within World Health
Organisation standards for fresh water. The study of the geology of
the warm spring reveals that the study area is underlain by a group of
slightly migmatised to non-migmatised paraschists and meta-igneous
rocks. Also, concentration levels of selected heavy metals, (Copper,
Cadmium, Zinc, Arsenic and Cromium) were determined in the water
(ppm) samples. Chromium had the highest concentration value of
1.52ppm (an average of 49.67%) and Cadmium had the lowest
concentration with value of 0.15ppm (an average of 4.89%).
Comparison of these results showed that, their mean levels are within
the standard values obtained in Nigeria. It can be concluded that both
warm and spring water are safe for drinking.
Abstract: This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.
Abstract: Industries produce millions of cubic meters of effluent
every year and the wastewater produced may be released into the
surrounding water bodies, treated on-site or at municipal treatment
plants. The determination of organic matter in the wastewater
generated is very important to avoid any negative effect on the
aquatic ecosystem. The scope of the present work is to assess the
physicochemical composition of the wastewater produced from one
of the brewery industry in South Africa. This is to estimate the
environmental impact of its discharge into the receiving water bodies
or the municipal treatment plant. The parameters monitored for the
quantitative analysis of brewery wastewater include biological
oxygen demand (BOD5), chemical oxygen demand (COD), total
suspended solids, volatile suspended solids, ammonia, total oxidized
nitrogen, nitrate, nitrite, phosphorus and alkalinity content. In
average, the COD concentration of the brewery effluent was 5340.97
mg/l with average pH values of 4.0 to 6.7. The BOD5 and the solids
content of the wastewater from the brewery industry were high. This
means that the effluent is very rich in organic content and its
discharge into the water bodies or the municipal treatment plant could
cause environmental pollution or damage the treatment plant. In
addition, there were variations in the wastewater composition
throughout the monitoring period. This might be as a result of
different activities that take place during the production process, as
well as the effects of peak period of beer production on the water
usage.
Abstract: Anaerobic modeling is a useful tool to describe and
simulate the condition and behaviour of anaerobic treatment units for
better effluent quality and biogas generation. The present
investigation deals with the anaerobic treatment of brewery
wastewater with varying organic loads. The chemical oxygen demand
(COD) and total suspended solids (TSS) of the influent and effluent
of the bioreactor were determined at various retention times to
generate data for kinetic coefficients. The bio-kinetic coefficients in
the modified Stover–Kincannon kinetic and methane generation
models were determined to study the performance of anaerobic
digestion process. At steady-state, the determination of the kinetic
coefficient (K), the endogenous decay coefficient (Kd), the maximum
growth rate of microorganisms (μmax), the growth yield coefficient
(Y), ultimate methane yield (Bo), maximum utilization rate constant
Umax and the saturation constant (KB) in the model were calculated to
be 0.046 g/g COD, 0.083 (d¯¹), 0.117 (d-¹), 0.357 g/g, 0.516 (L
CH4/gCODadded), 18.51 (g/L/day) and 13.64 (g/L/day) respectively.
The outcome of this study will help in simulation of anaerobic model
to predict usable methane and good effluent quality during the
treatment of industrial wastewater. Thus, this will protect the
environment, conserve natural resources, saves time and reduce cost
incur by the industries for the discharge of untreated or partially
treated wastewater. It will also contribute to a sustainable long-term
clean development mechanism for the optimization of the methane
produced from anaerobic degradation of waste in a close system.