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

Md. Uzzal Mia, Tahmida Naher Chowdhury, Rabin Chakrabortty, Subodh Chandra Pal, Mohammad Khalid Al-Sadoon, Romulus Costache and Abu Reza Md. Towfiqul Islam ()
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Md. Uzzal Mia: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
Tahmida Naher Chowdhury: Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
Rabin Chakrabortty: Department of Geography, The University of Burdwan, Bardhaman 713104, India
Subodh Chandra Pal: Department of Geography, The University of Burdwan, Bardhaman 713104, India
Mohammad Khalid Al-Sadoon: Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Romulus Costache: Department of Civil Engineering, Transilvania University of Brasov, 5 Turnului Street, 500152 Brasov, Romania
Abu Reza Md. Towfiqul Islam: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

Land, 2023, vol. 12, issue 4, 1-26

Abstract: 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.

Keywords: flood risk; ADT; northwest Bangladesh; naïve Bayes; Padma River basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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