An open-source model based on the Sendai DRR framework for computing flood risk scores from publicly available data.
Data that could enable more effective disaster-risk response and management is scattered or siloed across different agencies, at different scales and formats, making it difficult for decision-makers and relevant stakeholders to make data-informed decisions. The availability of good quality, machine-readable, and interoperable data is crucial for effective climate action and disaster response. However, in many contexts this data is fragmented across agencies, making timely, data-informed decisions difficult.
This repository contains the risk-score model of the IDS-DRR pipeline: a configurable system that combines climate, infrastructure access, losses & damages, procurement, and demographic data into a single composite flood risk score at district and sub-district (block/tehsil) levels. It is designed to be adapted to any geography for which the required input variables are available.
Originally developed for the state of Assam, India, this generic version of the codebase abstracts geography- and source-specific assumptions into configuration files so that the same scripts can be applied to other states, regions, or countries.
For programme background and methodological discussion, see the project report.
This work directly supports:
- SDG 13 — Climate Action, especially target 13.1 (strengthen resilience and adaptive capacity to climate-related hazards) and indicator 13.1.1 (number of people affected by disasters).
- SDG 11 — Sustainable Cities and Communities, especially target 11.5 (reduce deaths and economic losses caused by disasters) and target 11.b (integrated policies for resilience to disasters).
By producing transparent, reproducible risk scores at granular administrative levels, the project enables agencies to plan mitigation, target relief, and audit historical response patterns against measured exposure and vulnerability.
risk-score-model-generic/
├── scripts/ Factor and aggregation scripts
├── config/ TOML configuration files
├── data/ Sample inputs and outputs
├── assets/ Lookup tables and map boundary tooling
│ └── Maps/ map_exporter.py + map_transformer.py + GeoJSON examples
├── docs/ Methodology documentation
│
├── CITATION.cff Citation metadata
├── LICENSE GNU AGPL v3.0
├── README.md
└── requirements.txt Python dependencies
This repository covers the modelling layer only. Data acquisition is handled by a companion repository — see Data inputs below.
flowchart LR
subgraph DataLayer [Data Sources]
S1[Rainfall]
S2[Inundation]
S3[Demographics]
S4[Government tenders]
S5[Losses and Damages]
end
DataLayer --> M[MASTER_VARIABLES.csv<br/>One row per unit per month]
M --> H[hazard.py]
M --> E[exposure.py]
M --> V[vulnerability.py]
M --> G[govtresponse.py]
H --> T[topsis_riskscore.py<br/>TOPSIS aggregation]
E --> T
V --> T
G --> T
T --> O[risk_score_final_district.csv]
The four factor scripts (hazard.py, exposure.py, vulnerability.py, govtresponse.py) are independent and can run in any order. The TOPSIS script then combines their outputs into a single composite risk score weighted by hazard, exposure, vulnerability, and government response.
For a step-by-step walkthrough — including the input schema, configuration options, and per-script methodology — start with docs/getting_started.md.
- Python 3.11 or later
- pip and a virtual-environment tool of your choice
git clone https://github.com/CivicDataLab/risk-score-model-generic.git
cd risk-score-model-generic
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txtpython scripts/hazard.py
python scripts/exposure.py
python scripts/vulnerability.py
python scripts/govtresponse.py
python scripts/topsis_riskscore.pyEach script reads data/MASTER_VARIABLES.csv (or the file named in your config) and writes a CSV under data/. The final composite is risk_score_final_district.csv.
To adapt the model to a new geography, follow docs/getting_started.md.
Configuration TOML files are available under config/. The loader (config/loader.py) merges base_config.toml with the script-specific file at runtime:
| File | Controls |
|---|---|
base_config.toml |
Input file path and shared column names (object_id, timeperiod) |
hazard_config.toml |
Hazard variables, quantile thresholds, output classes |
exposure_config.toml |
Exposure variables and class boundaries |
vulnerability_config.toml |
DEA input/output variables and polarity inversions |
govtresponse_config.toml |
Response variables and fiscal-year start month |
topsis_config.toml |
Factor weights, indicator aggregation rules, district lookup |
Adapting the model to a new geography is mostly a matter of editing these TOMLs to match your input data. Code changes are needed only when adding new factor variables or modifying the methodology.
| Document | Contents |
|---|---|
getting_started.md |
End-to-end guide for adapting the model to a new geography |
score_hazard.md |
Flood Hazard methodology |
score_exposure.md |
Exposure methodology |
score_vulnerability.md |
Vulnerability methodology (DEA) |
score_government_response.md |
Government Response methodology |
topsis_risk_score.md |
TOPSIS composite score and final output |
assets/Maps/scripts/ |
admin-boundary download and transformation tooling for India |
The model consumes a single tabular file (MASTER_VARIABLES.csv) — one row per geographic unit per month. Each row carries the variables needed by the four factor scripts (rainfall, inundation, population, infrastructure, damages, expenditure).
The acquisition, cleaning, and joining of those variables is handled by a companion repository:
➡️ CivicDataLab/flood-data-ecosystem-generic
That repository contains per-source extractors for the Indian Meteorological Department (IMD), ISRO Bhuvan, WorldPop, NASADEM, Mission Antyodaya, BharatMaps, WRIS, and government tender portals. Its output is the master CSV that this model consumes.
For testing and demonstration, this repository ships with a small sample dataset in data/ so the model can be run end-to-end without first running the data pipeline.
All inputs and outputs use non-proprietary, machine-readable formats:
- Tabular data — CSV (UTF-8) with documented schemas
- Geometries — GeoJSON in EPSG:4326 (WGS 84)
- Configuration — TOML
- Documentation — Markdown
Output columns and their semantics are documented inline in each methodology document and summarised in data/data_dictionary.csv.
If you use this work in research or operational practice, please cite it. A CITATION.cff file is included for reference managers; in plain text:
CivicDataLab. (2026). Intelligent Data Solution for Disaster Risk Reduction — Risk Score Model (Generic). https://github.com/CivicDataLab/risk-score-model-generic
All source code in this repository is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). The full license text is in LICENSE.
Sample and derived datasets included in this repository are released under the Creative Commons Attribution 4.0 International license (CC-BY 4.0), unless a more restrictive licence applies to a specific upstream source (in which case the upstream licence governs that file).
This repository is in beta. The Assam-specific deployment is in production at scale; the generic refactor that lives here is being validated against new geographies. Interface and configuration schemas may still change between minor versions.
CivicDataLab · info@civicdatalab.in · https://civicdatalab.in