Flood Susceptibility Projections for Eastern Himalayan Region Under Climate Scenarios

Soumya Paramanik's research dataset investigates the impact of climate change on flood susceptibility using four deep learning models. The dataset likely contains model outputs and variable importance analyses for flood risk under three Shared Socioeconomic Pathways (SSP 245, SSP 370, SSP 585) for the year 2100. It was last updated on April 24, 2026.

Use Cases

• Comparing flood risk predictions from different deep learning models (DLNN, ANN, Deepboost, XGBoost) based on the description.
• Analyzing the influence of rainfall, slope, and land use/land cover (LULC) on flood susceptibility based on the variable importance analysis.
• Evaluating the escalation of flood risks under different emission scenarios (SSP 245, SSP 370, SSP 585) for the year 2100.
• Identifying high-risk zones for targeted mitigation efforts based on the detailed outputs from DLNN and ANN models mentioned.

Strengths

• Includes projections from four distinct deep learning models (DLNN, ANN, Deepboost, XGBoost).
• Analyzes flood susceptibility under three specific climate scenarios (SSP 245, SSP 370, SSP 585).
• Identifies key influencing variables (rainfall, slope, LULC) through importance analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is a single 4.4 MB DOCX file, which may require extraction of underlying data.

Provenance

Source

Soumya Paramanik via figshare.

Collection Method

Likely generated from research modeling flood susceptibility using deep learning algorithms and climate projections.

Time Range

Projections for the year 2100.

Freshness

Last updated 2026-04-24 05:40:03; freshness should be verified.

Geography

Eastern Himalayan region.