“…The review (Arakawa Martins et al, 2022) pointed to a vast variety of modelling approaches explored in the field, such as Bayesian classification and inference Auffenberg et al, 2018;Lee et al, 2019), Fuzzy Classification using the Wang-Wendel model (Pazhoohesh and Zhang, 2018;Aguilera et al, 2019;Jazizadeh et al, 2014b), and Machine Learning techniques. The latter includes more interpretable approaches such as Classification Trees (Aryal and Becerik-Gerber, 2020), or less transparent but relatively more accurate techniques such as Gaussian Process Classification (Guenther and Sawodny, 2019;Fay et al, 2017), Gradient Boosting Method (Lee and Ham, 2020), Support Vector Machine (Aryal and Becerik-Gerber, 2019;Jiang and Yao, 2016;Lu et al, 2019), Random Forest (Jayathissa et al, 2020;Aryal et al, 2021;Lu et al, 2019), K-Nearest Neighbours (Aryal and Becerik-Gerber, 2019;Aryal et al, 2021) and Artificial Neural Networks (Kim, 2018;Shan et al, 2020). Artificial Neural Networks (ANNs), specifically, have shown promising results.…”