Research Publications (Engineering and Built Environment)
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Item Hydrological characterization of twelve water catchments in Nigeria(AJER, 2014-01) Afolayan, S.O.; Adeyemo, Josiah; Otieno, Fredrick Alfred O.; Ojo, Olumuyiwa I.Twelve water catchments (WCs) in Ogbomosho, south west of Nigeria were evaluated for their hydrological characterization with respect to domestic and irrigation activities. Both physiochemical and biological parameters (limnological properties) were determined which include pH, total alkalinity (TA), CO32-, HCO3-, NO3-N, SO42-, N, P, K, Na, Ca, Mg, dissolved oxygen (DO), electrical conductivity (ECw), biochemical oxygen demand (BOD), total solids (TS), total dissolved solids (TDS), chlorophyll a,b,c and phaeophytin. Temperature fluctuation of the water catchments was measured in-situ to avoid samples coming into contact with the surrounding air using mercury in glass thermometer. Soil samples collected from the bottom of the water catchments were determined for chemical properties such as N, P, K, Na, Ca, Mg, and SO4-2 following recommended procedures. These parameters were investigated based on the perceived research consent of their efficacy in characterizing water catchments hydrologically along safety and pollution divides. The limnological properties were configured into ranking compared with standards to evaluate the degree of contamination or suitability of the WCs for domestic and irrigation purposes. Results obtained indicated pH values of the catchments ranging from 5.8 to 7.4 with corresponding TA between 0 and 296 mgL-1 suggesting high level of dissolved carbon dioxide (DCO2) and traces of untreated wastewater in most of the catchments. Based on ranking of the limnological properties of the WCs, WC4, WC5, WC6 recorded indices between 65 and 95 signifying that cumulatively these three WCs were more prone to pollution and could affect human health at consumption while WC2, WC3, WC 7 and , WC 10, aligned between 95 and 120 indicating mild to medium pollution and WC1, WC 11, and WC 12 oscillated between 120 to 145 picturing WCs approaching standards (132) while WC8 ranged between 145 and 170 revealing WC 8 as catchment with little or no tendency for hazards at drinking. Similarly, WC2, WC8, recorded soil reference (SR) between 50 and 60 suggesting possible interference of organic decomposition between the soil stratum and water in the catchment, however, WC1, WC4, WC5, WC6, WC7 and WC 12 recorded SR between 40 and 50 showing possible adjustment of the soils in the WCs of various salinity levels and WC 3, WC9, WC 11 revolving between 30 and 40 projecting the WCs with minimal pollution. Moreover, WC 10 only recorded value between 20 and 30, an inference of the soil stratum void of absolute contamination. Generally, WC2, WC8, appeared polluted both in limnological properties and basic soil conditions while WC9, WC10, WC11, and WC3 reflected high scale of ranking on limnological properties with low scale of SR possibly indicating little or no interaction between the soil base and the water in the catchments. Contrary wise, WC6 was high in SR but low in limnological properties. This trend suggests the presence of oxygen saturation in some of the WCs. Overall results indicated that WC4, WC5, WC6 require major water treatment prior to its usage for irrigation to avoid salt deposition at the crop root base, while WC2, WC3, WC7, WC9 and WC10 were considered relatively safe for drinking. WC1, WC11, WC2 requires some measure of precaution before drinking, however, WC12, and WC 8 could be consumed with little or no fear of infection.Item Analysis of temperature and rainfall trends in Vaal-Harts irrigation scheme, South Africa(AJER, 2014) Adeyemo, Josiah; Otieno, Fredrick Alfred O.; Ojo, Olumuyiwa I.Abstract: - Agriculture is crucially dependent on the timely availability of adequate amount of water and a conducive climate. Temperature and rainfall patterns impact the availability of water for agricultural uses. Therefore, temperature and rainfall are twin important environmental factors in agricultural activities such as tillage, planting, irrigation and mechanization. The characteristics of the Vaal-Harts temperature data for year 1996 to 2010 and rainfall data for year 1983 to 2010 were examined in this study using statistical techniques. Basic statistical properties of the data were determined using the mean, variance, coefficient of variation and Pearson’s correlation coefficient. Temperature and rainfall observations with the average of about 17.44 were used. The minimum and maximum temperatures recorded were 9.720C and 23.520C. The Coefficient of variation (CV) was found to be about 29.59. Variance is a measure of how far a set of numbers is spread out; and the variance of this set of observations is 26.625. The average yearly temperature increases insignificantly by a constant of about 0.117 (p = 0.163; 95% CI: -0.054 – 0.288), while rainfall shows decreasing trend annually which means that the dry season will be drier. The involvement of non-zero values in the serial correlation indicated the significance of the deterministic component in the data. The results of this analysis enhance our understanding of the characteristics of air temperature and rainfall in the study area for effective planning of farming operations.Item Review of three data- driven modelling techniques for hydrological modelling and forecasting(PSP, 2014) Oyebode, Oluwaseun Kunle; Otieno, Fredrick Alfred O.; Adeyemo, JosiahVarious modelling techniques have been proposed and applied for modelling and forecasting of hydrological sys-tems in different studies. These modelling techniques are majorly categorized into two namely, process-based and data-driven modelling techniques. While the process-based techniques provides detailed description of hydro-logical processes, data-driven techniques however de-scribe the behaviour of hydrological processes by taking into account only limited assumptions about the underly-ing physics of the system being modelled. Although, process-based techniques have been widely applied in numerous hydrological modelling studies, the application of data-driven modelling techniques on the other hand has not been fully embraced in the hydrological domain. This paper provides a comprehensive review of several stud-ies relating to three data-driven modelling techniques namely, K-Nearest Neighbours (K-NN), Model Trees (MTs) and Fuzzy Rule-Based Systems (FRBS). Modern trends with respect to their applications in hydrological model-ling and forecasting studies are also discussed. The struc-ture of this review encapsulates an introduction to each of the modelling techniques, their applications in hydrological modelling and forecasting, identification of areas of con-cern in their use, performance improvement methods, as well as summary of their advantages and disadvantages. The review aims to make a case for the application of data-driven modelling techniques by discussing the benefits em-bedded in its integration into water resources applications.Item Comparison of two data-driven modelling techniques for long-term streamflow prediction using limited datasets(SCIELO, 2015-09) Oyebode, Oluwaseun Kunle; Adeyemo, Josiah; Otieno, Fredrick Alfred O.This paper presents an investigation into the efficacy of two data-driven modelling techniques in predicting streamflow response to local meteorological variables on a long-term basis and under limited availability of datasets. Genetic programming (GP), an evolutionary algorithm approach and differential evolution (DE)-trained artificial neural networks (ANNs) were applied for flow prediction in the upper uMkhomazi River, South Africa. Historical records of streamflow, rainfall and temperature for a 19-year period (1994-2012) were used for model design, and also in the selection of predictor variables into the input vector space of the model. In both approaches, individual monthly predictive models were developed for each month of the year using a one-year lead time. The performances of the predictive models were evaluated using three standard model evaluation criteria, namely mean absolute percentage error (MAPE), root mean-square error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models (MAPE: 3.64%; RMSE: 0.52: R2: 0.99) during the validation phase when compared to the ANNs (MAPE: 93.99%; RMSE: 11.17; R2: 0.35). Generally, the GP models were found to be superior to the ANNs, as they showed better performance based on the three evaluation measures, and were found capable of giving a good representation of non-linear hydro-meteorological variations despite the use of minimal datasets.