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Statistical analyses reveal information of the hydrodynamics and environments of deposition of sedimentary rocks. The sediments of the outcrop sections from Uwakande 1, Obubra southeastern Nigeria were studied for their textural variations. Grain size analysis, and pebble morphogenesis carried out shows that the sediments are coarse to very coarse in size, poorly sorted, and very positively skewed. This in conjunction with the various bivariate plots employed, aid in discriminating between beach and river depositional environments. Pebble morphometric analysis of the conglomerates showed that the mean values of the various morphometric parameters range as follows: flatness ratio (S/L = 0.397 to 0.514), elongation ratio (I/L = 0.657 to 0.784), maximum projection sphericity (ΨP = 0.605 to 0.899), Oblate-Prolate index (ŌP = -1.738 to 1.594), coefficient of flatness (39.70 to 51.37), and roundness (-0.367 to 0.722). This study is significant in providing evidence of fluvial conditions during the deposition of the Eze-Aku Formation in the Turonian, and reveals the type of transporting medium; the mode of sediment deposition; as well as the environment into which the sediments encountered in the study area were deposited.

This paper examined the efficiency of artificial neural network (ANN) and multivariate linear regression (MLR) models in the prediction of groundwater quality parameters such as ecological risk index (ERI), pollution load index (PLI), metal pollution index (MPI), Nemerow pollution index (NPI), and geoaccumulation index (Igeo). 40 groundwater samples were collected systematically and analyzed for mainly heavy metals. Results revealed that adopting measured parameters is effective in modeling the parameters with high level of accuracy. Contamination factor results reveal that Ni, Zn, Pb, Cd, and Cu have relatively low values <1 within the region while the Iron values ranged from low contamination to very high contamination (>6). PLI, MPI, and ERI results indicated low pollution. NPI results indicated that the majority of the samples were heavily polluted. Quantification of Contamination results revealed that most of the sample's quality was geogenically influenced. Igeo results revealed that most of the samples had extreme pollution. The health risk assessment results revealed that children are substantially prone to more health risk more than adults. The ANN and MLR models showed a high effective tendency in the prediction of ERI, PLI, MPI, NPI and Igeo. Principal Component Analysis results showed appreciable variable loadings while the Correlation matrix results reveal that there exists weak and positive correlation amongst elements. Based on the outcome of this study, this research recommends the use of ANN and MLR models in the prediction of groundwater quality parameters as they yielded positive, reliable, acceptable, and appropriate accuracy performances.

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