Abstract
Despite some well-known limitations of artificial neural network (ANN), the basic ANN and its derivatives are frequently used to predict the removal efficiency of different heavy metals like arsenic (As) using various adsorbents, including bio-adsorbents. Notable examples of applying non-neural network (NN) approaches, such as support vector regression (SVR) and random forest (RF), are almost non-existent in theliterature. In the current study, the suitability of these modules to predict As removal efficiency was investigated based on seven independent experimental data sets. The SVR and RF experiments with the merged datasets of experimental and interpolated data points demonstrated their effectivity for the prediction. Specifically, the RF method achieved 97.3% Spearman's rank correlation coefficient (SRCC) and 95.9% R-2 on average, whereas its SVR counterpart exhibited average performances of 96.3% SRCC and 93.9% R-2 on a hold-out test set. A general method based on the natural cubic spline technique was introduced to interpolate the experimental data points. Compared to the ANN methodology, the non-NN approaches involved tuning fewer hyperparameters and easier training processes in the R open-source framework. Furthermore, this kind of economical application of the machine learning algorithms allowed ranking the experimental parameters based on their relative contribution in the As removal efficiency. The study showed that the following parameters are the most influential ones, in decreasing order: pH, adsorbent dose, temperature, agitation time, and initial As concentration.