*5.3. Building the Flash Flood Models*

Different hybrid models, namely ABM-CDT, Bag-CDT, Dag-CDT, MBAB-CDT, and a single classifier CDT were developed in this step using training dataset. Out of these methods, ABM-CDT is a combination of AdaBoostM1 ensemble and CDT classifier, Bag-CDT is a combination of Bagging ensemble and CDT, Dag-CDT is a combination of Dagging and CDT, and MBAB-CDT is a combination of MultiBoostAB and CDT. In these hybrid models, ensemble techniques were used to optimize the training dataset which was then used as input data in CDT classifier for flash flood susceptibility assessment. To construct these models, internal parameters should be selected and optimized to get the best performance of the models. More specifically, in CDT, initial parameters such as batch size, initial count, maximum of depth, minimum total weight of instances in a leaf, minimum proportion of variance, number of folds and seed were selected as 100, 0.0, −1, 2.0, 0.001, 3, 1, respectively. In ABM-CDT, initial parameters such as batch size, number of iterations, seed and weight of threshold were selected as 100, 10, 1, and 100, respectively. In Bag-CDT, initial parameters such as batch size, number of execution slots, number of iterations, and seed were selected as 100, 1, 15, and 1, respectively. In Dag-CDT, initial parameters such as batch size, number of folds, and seed were selected 100, 10, and 1, respectively. In MBAB-CDT, initial parameters such as batch size, number of iterations, number of subcommittees, seed, and weight of threshold were selected 100, 20, 3, 1, and 100, respectively. The values of these initial parameters of the models were determined by the trial-error process. This step was carried out using the packages and codes included in the Weka software.

#### *5.4. Validation of the Models*

Validation of the models was carried out on both training and testing datasets using various criteria such as PPV, NPV, SST, SPF, ACC, Kappa, RMSE, and AUC. While validation using training dataset shows the goodness-of-fit of the models, validation using testing datasets shows predictive capability of the models. This step was carried out using the packages and codes included in the Weka software.
