Artificial neural network modeling and exposure assessments: a new scaling approach

Abstract

Conventional modeling approach such as regression has not much ability to incorporate nonlinearity of environmental covariates during different categories of estimations. Nonlinear information is considered as outliers in prediction while fitting in the regression models. The ANN models are robust due to adaptive neuro computing to consider these kinds of parameters in the scaling and instrumentation process due to gradient neural weight training mechanism. Raised level of ambient particulate matter having size PM₁₀ and PM_2.5 is a serious matter of concern for children having age group 8 to 14 years. A study has been conducted in North West side of Asia continent for three years (2013 to 2016) where sampling was done to measure ambient PM level, physiological parameters of 600 children and covariate data. A hybrid recurrent neural network (HRNN) has been developed for exposure assessment with the help of Gamma Test. Based on Root Mean Square Error and Mean Absolute Percentage Error scale, the trends in physiological parameters were estimated. The developed model strategy, will help research community and policy-makers to feed time series data in the model for spatiotemporal analysis rather than conventional statistical methods.

Keywords:

• Children Health
• particulate matter
• lung functions
• neural network modeling

Acknowledgement

Author is thankful to Dr. Susheel Mittal, Dr. Ravinder Agarwal Thapar University, Patiala and team members for providing data and analysis. Author is also acknowledging the guidance of Dr. Loviraj Gupta Dean LPU, Phagwara for their faith and valuable suggestions and resources.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Indian Council of Medical Research (ICMR) committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.