National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh

Abstract

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system.

Keywords:

• Flood risk assessment
• flood susceptibility mapping
• hybridized deep neural network
• hybridized support vector regression
• genetic algorithm
• fuzzy analytic hierarchy process
• random forest

Data and code availability statement

The data and codes that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Ministry of Post, Telecommunication and Information Technology, Bangladesh through ICT Innovation Fund (2020-21) round 3: Grant Number 12.