Morenike Oluwatoyin Folayan, Elisa Maria Rosa de Barros Coelho, Imen Ayouni, Arthemon Nguweneza, Ola Barakat Al-Batayneh, Hamideh Daryanavard, Duangporn Duangthip, Ivy Guofang Sun, Arheiam Arheiam, Jorma I Virtanen, Balgis Gaffar, Maha El Tantawi, Robert J Schroth, Carlos Alberto Feldens
Association between early childhood caries and parental education and the link to the sustainable development goal 4: a scoping review
BMC oral health
groundwater pollution identification inverse problem solving inverse problem using regular methods simulationoptimization stochastic statistical approaches requires repeatedly calling simulation model forward calculations timeconsuming process currently problem often solved building surrogate model simulation model however surrogate model intermediate step regular methods simulationoptimization method also require creation solution optimization model minimum objective function adds complexity time inversion task presents obstacle achieving fast inversion present study extreme gradient boosting xgboost method back propagation neural network bpnn method used directly establish mapping relationships output input simulation model could directly obtain inversion results variables identified pollution sources release histories hydraulic conductivities based actual observational data fast inversion addition consider uncertainty observation data noise inversion accuracy two machine learning methods compared method higher precision selected uncertainty analysis results indicated bpnn xgboost methods could perform inversion tasks well mean absolute percentage error mape 415 139 respectively using bpnn better accuracy uncertainty analysis maximum probabilistic density value selected inversion result mape 213 obtained inversion results different confidence levels decision makers groundwater pollution prevention control choose different inversion results according needs