Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology

Mia, Md. Uzzal; Rahman, Mahfuzur; Elbeltagi, Ahmed; Abdullah-Al-Mahbub, Md.; Sharma, Gitika; Islam, H. M. Touhidul; Pal, Subodh Chandra; Costache, Romulus; Islam, Abu Reza Md. Towfiqul; Islam, Md. Monirul; Chen, Ningsheng; Alam, Edris; Washakh, Rana Muhammad Ali

doi:10.1080/10106049.2022.2112982

Cited by 10 publications

(10 citation statements)

References 122 publications

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“…Accurately estimating FS zones is a great challenge for developing realistic and efficient mitigating strategies [39]. Therefore, the FSM has become the global management basis [4,39,61]. As a result, experts are constantly looking for innovative and dependable strategies to produce detailed and accurate findings that can be used to propose flood control measures [4,23,48,61,113].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the FSM has become the global management basis [4,39,61]. As a result, experts are constantly looking for innovative and dependable strategies to produce detailed and accurate findings that can be used to propose flood control measures [4,23,48,61,113]. Various statistical and machine learning tools were used to model FS.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 3 shows details on the flood-causing agents. Elevation, slope angle, aspect, profile curvature, plan curvature, SPI, TWI, STI, river density, distance to the river, flow accumulation, and flow direction datasets were retrieved from an advanced land-observing satellite (ALOS)based digital elevation model (DEM) with a resolution of 30 m. Based on past literature, the ALOS-based DEM was the most accurate way to develop the raster layers of these flood conditioning variables [4]. Based on data from [89] and [90], a map illustrating mean annual rainfall from 2000 to 2021 was created in a GIS environment by using kriging spatial interpolation tools (https://chrsdata.eng.uci.edu/) [89].…”

Section: Flood Conditional Variable Preparationmentioning

confidence: 99%

“…The tolerance (TOL) and the variance inflation factor (VIF) were used to evaluate the multicollinearity of all causal variables, since linear collinearity lowers the performance of predictive methods [4][5][6]. In fact, if the TOL or VIF is higher than 5.0 and lower than 0.1, this indicates multicollinearity in the conditioning variable [21].…”

Section: Multicollinearity Assessmentmentioning

confidence: 99%

“…Owing to climatic variability, such as a warmer climate and excessive rainfall, flooding occurs regularly in various locations globally [1][2][3][4]. Flood risks increase as the world's population, industrialization, and agricultural intensification accelerate [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer

Mia

Chowdhury

Chakrabortty

et al. 2023

Land

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We developed a novel iterative classifier optimizer (ICO) with alternating decision tree (ADT), naïve Bayes (NB), artificial neural network (ANN), and deep learning neural network (DLNN) ensemble algorithms to build novel ensemble computational models (ADT-ICO, NB-ICO, ANN-ICO, and DLNN-ICO) for flood susceptibility (FS) mapping in the Padma River basin, Bangladesh. The models consist of environmental, topographical, hydrological, and tectonic circumstances, and the final result was chosen based on the causative attributes using multicollinearity analysis. Statistical techniques were utilized to assess the model’s performance. The results revealed that rainfall, elevation, and distance from the river are the most influencing variables for the occurrence of floods in the basin. The ensemble model of DLNN-ICO has optimal predictive performance (AUC = 0.93, and 0.91, sensitivity = 0.93 and 0.92, specificity = 0.90 and 0.80, F score = 0.91 and 0086 in the training and validation stages, respectively) followed by ADT-ICO, NB-ICO, and ANN-ICO, and might be a viable technique for precisely predicting and visualizing flood events.

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