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Despite the high level of interpretability of the Classification and Regression Tree (CART) as one of the widely-used data mining models, its results usually are less accurate than other models. To address the low accuracy of the CART model, this paper developed two new and enhanced CART-based algorithms that can provide both high interpretability and high accuracy for flood susceptibility mapping. In the developed models, various hyperparameters of the CART were explicitly tuned using the genetic algorithm (GA) and grid search (GS) methods. Eleven variables and 222 flood locations were used in Sardabroud watershed in Iran for modelling. The results showed area under the receiver operating characteristic curve (AUROC) values of 0.884, 0.927 and 0.908, respectively, for the CART, CART-GS and CART-GA models in the validation run. The results showed that the developed models can produce the output that satisfies both accuracy and interpretability criteria. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions ( volume / capacity < 0.5 ) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (BAT) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+BAT model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+BAT model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution. [ABSTRACT FROM AUTHOR]