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Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high levels of chlorine and/or other organic acids that act as catalysts or precursors in PCDD/Fs formation. This study aims to predict PCDD/Fs emissions in peat soil using an artificial neural network (ANN) approach based on limited emission data and selected physico-chemical properties. The ANN's prediction performance is affected by uncertainties in its initial connection weights. To improve prediction performance, an optimisation algorithm, termed differential evolution (DE), is used to optimise the ANN's initial connection weights and bias. The study adopts several ANNs with fixed architecture to predict PCDD/Fs emissions, each consisting of a multilayer perceptron (MLP) with a backpropagation algorithm. Eight input variables and one output variable were adopted to train and test various neural network architectures using real-world datasets. The model optimisation procedure was conducted to ascertain the network architecture with the best predictive accuracy. The evolved ANN based on 5 hidden neurons, with the assistance of self-adaptive ensemble-based differential evolution with enhanced population sizing (SAEDE-EP), successfully produced the lowest MSE test (6.1790 × 10−3) and highest R2 (0.97447) based on the mean among the other HNs. An evolutionary-optimised ANN-based methodology is a viable solution to predict PCDD/Fs in peat soil. It is cost-effective for pollution control, environmental monitoring and capable of aiding authorities prevent PCDD/Fs exposure, e.g., during a fire. [Display omitted] • PCDD/Fs prediction model is built based on an evolutionary-optimised ANN. • Adopted limited datasets of PCDD/Fs emissions and physico-chemical of peat samples. • Differential evolutionary algorithm improved accuracy and error estimates. • Cost-effective solution for pollution and environmental monitoring. [ABSTRACT FROM AUTHOR]

The conventional method for determining polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) emission concentrations from municipal solid waste incinerators (MSWIs) is accurate but complex, costly, and time-consuming. In this study, we utilized machine learning approaches to model International Toxic Equivalent Quality (I-TEQ) values of PCDD/Fs through chlorobenzene (CBz). Sampled data from a MSWI with a grate furnace and three MSWIs with fluidized bed furnaces were randomly divided into the training and test set. A two-step data preprocessing consists of Box-Cox transformation and standardization was applied to improve the model performances. The models including artificial neural network (ANN) and decision tree (DT). With the help of bagging, ANN and DT were stacked into bagging-based ensemble neural networks (BGNN) and random forest (RF) to improve the accuracy and robustness. BGNN was proved to be the best model with the relatively good perfomance in the test set. The Shapley additive explanation (SHAP) method was employed to improve the interpretability of BGNN. The results show that 1,2,3-trichlorobenzene (TrCBz), 1,4-dichlorobenzene (DiCBz), 1,2,4-TrCBz, and 1,3-DiCBz make positive contributions to I-TEQ values. Employing machine learning models, particularly BGNN, can be an effective method to determine the emission of PCDD/Fs within a wide range of I-TEQ values and in multiple MSWIs with strong generalization ability. It is helpful to realize the on-line measurement and controlling of PCDD/Fs emission. • Applying machine learning approaches to model PCDD/Fs I-TEQ value from MSWIs. • Improving the property of artificial neural network and decision tree with bagging. • Bagging-based ensemble neural networks (BGNN) was proved to be the best model. • Neuron-based models outperform tree-based models. • Evaluating the effects of CBz congeners on the I-TEQ value through the SHAP method. [ABSTRACT FROM AUTHOR]