Abstract

The significance of total polyaromatic hydrocarbons (TPAH) determination in assessing the carcinogenicity of environmental samples for measuring the level of environmental pollution cannot be overemphasized. Despite the environmental danger of TPAH, its laboratory quantification is laborious, which consumes appreciable time and other valuable resources. This research work develops a computational intelligence-based model for the first time, which directly estimates and quantifies the level of TPAH of any environmental solid samples using total petroleum hydrocarbons descriptor that can be easily determined experimentally. The hyperparameters of the developed support vector regression (SVR)-based model are optimized using manual search (MS) approach and genetic algorithm (GA) search approach with Gaussian and polynomial kernel functions. Experimental validation of the developed model was carried out using samples obtained from the marine sediments of Arabian Gulf Sea. The future generalization and predictive strength of the developed models were assessed using correlation coefficient (CC), root-mean-square error, mean absolute error and mean absolute percentage deviation (MAPD). GA-SVR-Gaussian performs better than MS-SVR and GA-SVR-poly with performance enhancement of 63.89% and 536.32%, respectively, on the basis of MAPD as a performance-measuring parameter, while MS-SVR model performs better than GA-SVR-poly with performance improvement of 288.25% using MAPD to evaluate the model performance. The estimation accuracy and generalization strength of the developed models indicate the potential of the models in measuring the level of environmental pollution of oil-spilled area without experimental stress, while experimental precision is preserved.

中文翻译：

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支持向量回归模型预测土壤中的总多芳烃：绘制环境污染的人工智能系统

摘要

总聚芳烃（TPAH）测定在评估环境样品的致癌性以测量环境污染水平方面的重要性不可过分强调。尽管TPAH有环境危险，但其实验室量化工作十分繁琐，耗费大量时间和其他宝贵资源。这项研究工作首次开发了一种基于计算智能的模型，该模型使用可通过实验轻松确定的总石油烃描述符直接估算和量化任何环境固体样品的TPAH含量。使用人工搜索（MS）方法和遗传算法（GA）搜索方法以及高斯和多项式内核函数，优化了基于支持向量回归（SVR）的模型的超参数。使用从阿拉伯湾海域海洋沉积物中获得的样品对开发的模型进行实验验证。使用相关系数（CC），均方根误差，平均绝对误差和平均绝对百分比偏差（MAPD）评估了开发模型的未来通用性和预测强度。GA-SVR-Gaussian的性能优于MS-SVR和GA-SVR-poly，以MAPD作为性能衡量参数，其性能分别提高了63.89％和536.32％，而MS-SVR模型的性能优于GA -SVR-poly使用MAPD评估模型性能可提高288.25％的性能。