Huong Thi Thanh Ngo¹, Nguyen Duc Dam¹, Quynh-Anh Thi Bui¹, Nadhir Al-Ansari^2,*, Romulus Costache^3,4,*, Hang Ha⁵, Quynh Duy Bui⁵, Sy Hung Mai⁶, Indra Prakash⁷, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.022566

Abstract Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were… More >

Saeed Ahmed^1,2, Noor Gul^1,3, Jahangir Khan⁴, Junsu Kim¹, Su Min Kim^1,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5061-5082, 2022, DOI:10.32604/cmc.2022.023639

Abstract Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the… More >