Browsing by Author "Nwobia, Light"

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    Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming
    (Applied and Environmental Soil Science, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light
    Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. +ree evolutionary trials were executed, and three models were presented considering different permutations of the predictors. +e performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.
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    Prediction and performance analysis of compression index of multiple‑binder‑treated soil by genetic programming approach
    (Nanotechnology for Environmental Engineering, 2021) Onyelowe, Kennedy C.; Ebid, Ahmed M.; Nwobia, Light; Dao‑Phuc, Lam
    The use and its advantage in overcoming time and equipment needs of an evolutionary prediction technique known as the genetic programming have been studied using unsaturated sample of soft soil treated with multiple binders. The soil classified as weak and highly plastic was stabilized and multiple experiments were conducted to measure the effect of the dosages of the treatment on the selected properties. The geotechnics of the exercise showed that the studied parameters substantially improved with increased proportion of hybrid cement (HC) and nanostructured quarry fines (NQF). These measured selected properties were further deployed to predict the compression index of the soil. The prediction operation proposed four-model equation by the degree of importance, sensitivity and influence of the independent parameters. This shows eventually that plasticity index has the greatest sensitivity on the compression behaviour of clay soils. The performance analysis shows that the models have very low error with model trial 4 presented in Eq. 7: CGP C = (IP−Hc⋅NQF) ( part∕ max)NQF (Ip∕wmax) Ln(wmax+3.0) , showing the least error with more consideration for the influence of more of the selected variables. It also exhibited the highest degree of determination. Generally, GP has proven to be flexible, fast and able to predict models for engineering problems for use in design and performance study.