- English
Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study
assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area
notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several
innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine
Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating
Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE),
and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied
region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas.
Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang,
Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and
Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong
performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques
and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional
administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a
climate change adaptive response within the Brahmaputra River basin.
- English
