A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability

Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management systems are challenging for decision-makers. In this regard, data-driven methods can be considered robust approaches to modeling this problem. In this paper, three black-box data-driven models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR), and also three whitebox data-driven models, including the M5 model tree (M5MT), classification and regression trees (CART), and group method of data handling (GMDH), were developed for modeling and predicting landfill leachate permeability (k). Based on a previous study conducted by Ghasemi et al. (2021), k can be formulated as a function of impermeable sheets (IS) and copper pipes (CP). Hence, in the present study, IS and CP were adopted as input variables for the prediction of k and evaluated for the performance of the suggested black-box and white-box data-driven models. Scatter plots and statistical indices such as coefficient of determination (R-2), root mean square error (RMSE), and mean absolute error (MAE) were used for qualitative and quantitative evaluations of the effectiveness of the suggested methods. The outcomes indicated all of the provided models successfully predicted k. However, ANN and GMDH had higher accuracy between the proposed black-box and white-box data-driven models. ANN with R-2 = 0.939, RMSE = 0.056, and MAE = 0.017 was marginally better than GMDH with R-2 = 0.857, RMSE = 0.064, and MAE = 0.026 in the testing stage. Nevertheless, an explicit mathematical expression provided by GMDH to predict k was easier and more understandable than ANN.