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Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring

机译：基于多目标粒子群优化的基于苯监测的自适应神经模糊推理系统

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摘要

Air pollutants such as benzene (C6H6) have accelerated the rate of cancer among human beings. Currently, atmospheric contamination is measured using spatially separated networks with limited sensors. However, the expenses involving multiple sensors with varying sizes limit the operational efficiency. Therefore, in this paper, a novel multi-objective regression model is proposed to predict benzene concentration in the ambient air pollution data, without need to deploy actual sensors for benzene detection. It is possible because there is a relation among various atmospheric gasses and thus regression can be performed to measure C6H6 if the concentration level of other gasses is known. Proposed technique utilizes adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict C6H6 density in the air. PSO is employed to enhance the accuracy of ANFIS for runtime parameter tuning by calculating multi-objective fitness function which involves accuracy, root mean squared error and correlation (r). The proposed technique is tested on well-known publicly available air pollution datasets and on real-time primary dataset for quantitative analysis. Experimental results indicate that the proposed method consistently outperforms over available methods to predict C6H6 concentration in the atmosphere. Thus, it is well suitable to build self-dependable time and cost-effective benzene prediction model.
机译：苯（C6H6）等空气污染物加速了人类癌症率。目前，使用具有有限传感器的空间分离的网络测量大气污染。然而，涉及多个传感器的费用具有不同尺寸的尺寸限制了操作效率。因此，在本文中，提出了一种新的多目标回归模型，以预测环境空气污染数据中的苯容浓度，无需部署实际传感器以进行苯检测。由于存在各种大气气体之间存在各种大气气体之间的关系，因此如果已知其他气体的浓度水平，则可以执行回归以测量C6H6。所提出的技术利用自适应神经模糊推理系统（ANFIS）和粒子群优化（PSO）来预测空气中的C6H6密度。通过计算涉及精度的多目标适应性函数来提高运行时参数调整的ANFI的准确性，涉及准确性，根均匀的误差和相关性（R）。所提出的技术在众所周知的公知的空气污染数据集和实时初级数据集上进行测试，用于定量分析。实验结果表明，该方法始终如一地优于可用方法来预测大气中的C6H6浓度。因此，适合构建自我可靠的时间和成本效益的苯预测模型。

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