Mehtab Alam

TL;DR: In this paper, the authors used Bayesian regularization back propagation (BRBP) neural network, classification and regression trees (CART), a statistical model (STM) using the evidence belief function (EBF), and their ensemble models (EMs) for three time periods (2000, 2014, and 2017).

TL;DR: The proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation.

TL;DR: It is concluded that the proposed humanitarian aid information system (HAIS) will help humanitarian organizations and government agencies coordinate and perform relief operations effectively in the worst-hit regions across the country.

TL;DR: In this paper, the authors used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh.

TL;DR: In this article, the authors classified two impact models with respect to the pileup characteristics of the dead zone and employed the discrete element method to investigate the influences of the piling characteristics on the impact force of dry granular flow on a tilted rigid wall.