Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm. (15th August 2021)

• Record Type: Journal Article
• Authors: Rahman, Mahfuzur; Chen, Ningsheng; Islam, Md Monirul; Mahmud, Golam Iftekhar; Pourghasemi, Hamid Reza; Alam, Mehtab; Rahim, Md Abdur; Baig, Muhammad Aslam; Bhattacharjee, Arnob; Dewan, Ashraf
• Is Part Of: Journal of cleaner production. Volume 311(2021)
• Publication Date: 2021-08-15
• Subjects: Flood -- Coping mechanisms -- Emergency relief -- Hydrodynamic modeling -- Economic instability; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.127594
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: This study performs flood hazard mapping and evaluates community flood coping strategies. In addition, it proposes a humanitarian aid information system (HAIS) to enhance emergency support for flood victims. First, a flood hazard map was prepared using the hydrodynamic model (HM)–FLO 2D coupled with a machine learning algorithm (MLA)-scaled conjugate gradient neural network (SCG-NN). The performance of the MLA was evaluated using a validation dataset and statistical measures such as the mean square error (MSE: 0.080), root mean square error (RMSE: 0.282), and coefficient of determination (R 2 = 0.830). According to the generated flood hazard map, most of the study area was classified as low (47.85%) or moderate (27.47%) hazardous zones, whereas only a small portion was delineated as high (20.64%) or very high (4.04%) hazardous zones. The accuracy of the hazard map (HM-MLA) versus the ground truth was tested statistically and was found to be high. Second, an investigation of local flood management strategies revealed that the current information system is not well prepared for emergencies, including the quantification of emergency relief necessities. Therefore, an HAIS, which specifies hazard information and quantifies emergency aids (food items) for flood victims, can be an effective emergency preparedness tool. We calculated the required emergency aid considering satellite-derived flood data. Finally, we conclude that the proposed HAIS will help humanitarian organizations and government agencies coordinate and perform relief operations effectively in the worst-hit regions across the country. Graphical abstract: Image 1 Highlights: Machine learning and hydrodynamic models were integrated for flood hazard mapping. MSE, RMSE, and R 2 used to check the performance of machine learning algorithms. Humanitarian aid information system proposed to mitigate flood risk.