Non-carcinogenic health risk assessment and prediction of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles has been done. Thirty groundwater samples were collected systematically and analyzed for organic and heavy metal pollutants. The results of the analysis showed that the heavy metals and organic pollutants contributed to the pollution of groundwater resources in the locality. 63.3% of the entire water samples had As above the WHO standard, same as Fe (60%), Cr (100%), Pb (56.7%), E (16.7%), X (13.3%), B (40%). Correlation matrix results indicated a weak correlation. For the Principal Component Analysis, PC1 showed that 60% of the entire variable had loadings, PC2 had 40%, PC3 had 30%, PC4 had 10% loadings of parameters within the study area, and that organic pollutants were major contributors to the loadings. The Contamination factor, Pollution load index, Metal pollution index, Geoaccumulation index, Potential ecological risk index, Elemental Contamination Index, and Overall Metal Contamination Index showed no significant pollution, whereas the Heavy Metal Evaluation Index, Pollution Index of Groundwater results showed the worrisome impact of the anthropogenic activities on the groundwater quality. Health risk assessment showed that children are more at risk than adults as it related to taking polluted with a Hazard Quotient and Hazard Index trend is Cr > As > T > E > m-X > o-X > B > Pb > Cu > Fe. This trend is the same for both children and adults. Seven mathematical models were generated for the prediction of pollution indices. Based on the results, this study recommends regular monitoring of groundwater resources and the integration of ANN and MLR modeling approaches for the prediction of pollution indices.

使用人工神经网络和多线性建模原理对尼日利亚奥西西奥马周围地下水的非致癌健康风险评估和污染指数进行了预测。系统地收集了 30 个地下水样本，并分析了有机污染物和重金属污染物。分析结果表明，重金属和有机污染物是造成当地地下水资源污染的主要原因。全部水样中63.3%的As高于WHO标准，Fe（60%）、Cr（100%）、Pb（56.7%）、E（16.7%）、X（13.3%）、B（40%） ). 相关矩阵结果表明相关性较弱。对于主成分分析，PC1 显示整个变量的 60% 有载荷，PC2 有 40%，PC3 有 30%，PC4 在研究区域内有 10% 的参数载荷，并且有机污染物是负荷的主要贡献者。污染因子、污染负荷指数、金属污染指数、地累积指数、潜在生态风险指数、元素污染指数、总金属污染指数显示无显着污染，而重金属评价指数、地下水污染指数结果显示影响令人担忧人类活动对地下水水质的影响。健康风险评估表明，儿童比成人更容易受到污染，其风险指数趋势为Cr > As > T > E > mX > oX > B > Pb > Cu > Fe。这种趋势对于儿童和成人都是一样的。为预测污染指数生成了七个数学模型。根据结果​​，