Data Model

Context

The IDS-DRR platform aims to fill a need where authorities lack the data to make actionable insights when it comes to long term flood risk mitigation. The platform is designed based primarily on the UN’s SENDAI framework for Disaster Risk Reduction (DRR). Within this framework, flood risk is defined as being proportional to the following factors:

Factor	Definition	Example indicators
Exposure	Number of people and assets exposed to a disaster event	Total Population, total households, cultivated crop area
Hazard	The geographical characteristics that contribute to the likelihood of a natural disaster	Inundation, rainfall, distance from river
Vulnerability	The socio economic factors that increase the risk due to a disaster event or determine a population’s ability to recover from one	Access to infrastructure: roads, health centers, railways. Actual losses and damages due to disaster events
Coping Capacity	The ability of a population to respond to the disaster event and reduce its risk	Total Flood Tenders received, Restoration funds

The relationship between these factors and flood risk can be defined as follows:

Risk ∝ Exposure^a × Hazard^b × Vulnerability^c × (lack of coping capacity)^d

Where a, b, c and d are weightages currently provided to the different factor scores.

The data model for the IDS-DRR platform was built keeping this relationship in mind, and collates a variety of different sources to model risk scores for each factor.

Data Model Overview

The data model is run monthly and outputs 5 factor scores and a risk score for each revenue circle and district. All the scores range from 1-5, where 1 corresponds to lowest risk / hazard / vulnerability, and 5 corresponds to highest.

Exposure

Exposure indicates the total population and value of assets that is at risk during a disaster event. In IDS-DRR, this consists of the two indicators: total_households taken from Mission Antyodaya, and Sum_population, derived from UN WorldPOP.

Transformation of each indicator

Sum_population: As the Census figures have not been updated since 2011, population figures have been estimated using UN WorldPOP’s population dataset. Raster files from 2016-2020 are clipped to the region of analysis, and extrapolated using linear regression for 2021-24.
Total_households: Household figures for rural settlements are available in the Mission Antyodaya dataset. Figures for urban settlements are extrapolated from urban population figures.

Exposure Factor Score

To calculate the Exposure factor score for a particular month, the data for each indicator across all the revenue circle / tehsils are normalized using Min-max Scaler in order to scale the range of values from 0-1. The individual feature scores are then added, and mean and standard deviation of the resulting “sum” column are then calculated. The factor score is then assigned to each revenue circle based on z-score binning:

Factor Cumulative Value	Factor Score
`exposure['sum'] <= mean`	1
`exposure['sum'] > mean & <= mean + std`	2
`exposure['sum'] > mean + std & <= mean + 2*std`	3
`exposure['sum'] > mean + 2std & <= mean + 3std`	4
`exposure['sum'] > mean + 3*std`	5

Hazard

Hazard is defined by the geographical and hydro-meteorological characteristics that contribute to the likelihood of a natural disaster resulting in loss of life or property. It consists of the following datasets:

Drainage Density: A measure of how many rivers/streams are present in an area, derived from the Digital Elevation Model (DEM), extracted from NASADEM’s SRTM dataset. Only needs to be sourced once.
Rainfall: Daily rainfall figures are scraped from IMD using the IMDlib library, and transformed to extract mean_rain (average rainfall across a month) and max_rain (highest single instance of recorded daily rainfall).
Inundation: When available, inundation data is taken from Bhuvan’s flood database. The data comes in the form of raster images where each pixel corresponds to a 10m × 10m area. These images are scraped and transformed to obtain inundation_percentage and inundation_intensity. In some cases, raster images are obtained from SENTINEL SAR-1 satellite imagery.

Hazard Factor Score

To calculate the Hazard factor score for a particular month, the data for each indicator across all the revenue circle / tehsils are normalized using minmaxscaler. The mean and standard deviation for each feature is then calculated across all the revenue circles for the month. A score is then assigned to each feature based on the z-score binning method. To obtain the final hazard score, the arithmetic mean of the individual factor scores is taken and rounded to the nearest integer.

Vulnerability

Vulnerability is a measure of the resilience of a population, and indicates the actual impact of flood as experienced by the population. This resilience is influenced by the socio-economic conditions, infrastructure facilities present and the actual losses and damages due to disaster events.

Vulnerability Data Sources

Infrastructure: For the indicators schools_count, health_centres_count, rail_length, and road_length, data is extracted from BharatMaps ArcGIS REST Server. Other infrastructure indicators are derived from the Mission Antyodaya dataset including: Net sown area in hectares, average daily electricity availability in hours, Percentage of households without sanitation facilities and Percentage of households with piped water facilities.
Demographic: Indicators include mean_sexratio and sum_aged_population, estimated using the UN WorldPOP’s population dataset.
Losses and Damages: All losses and damages indicators are received directly from the State Disaster Management Authorities. In the case of Assam, this data is scraped from DRIMS reports.

Vulnerability Factor Score

To calculate the Vulnerability factor score, a method known as Data Envelopment Analysis (DEA) is utilized. DEA calculates an “efficiency score” for each spatial unit based on its ability to minimize losses and damages given its available infrastructure and vulnerable population.

The following transformations are made before running the DEA model:

The data range of each indicator is normalized using MinMaxScaler (0-1)
The “positive” input indicators are reversed (subtracted from 1)
The losses and damage indicators are all reversed, as the “output” is the ability of the spatial unit to reduce damage

The DEA model is then run (using Gurobi’s python implementation). The efficiency scores output by the model are then classified into 5 classes from 5 to 1 using Jenk’s Natural Breaks.

Coping Capacity / Government Response

Coping Capacity is indicative of the resources available to a revenue circle / tehsil to be able to respond to, or minimize the losses caused due to flood events.

Data Sources

E-tenders: Tenders scraped from each state’s GEPNiC platform. The scraped tenders are analyzed and classified into different categories depending on tender sources and usage, and geocoded to the relevant district / revenue circle. Indicators include: total_tender_awarded_value, SOPD_tenders_awarded_value, SDRF_tenders_awarded_value, RIDF_tenders_awarded_value, LTIF_tenders_awarded_value, CIDF_tenders_awarded_value, Preparedness Measures_tenders_awarded_value, Immediate Measures_tenders_awarded_value and Others_tenders_awarded_value.
State Disaster Relief Funds: Funds sanctioned by the state to districts for various flood preparedness and response measures, manually scraped from the annual SEC meeting minutes.

Coping Capacity Factor Score

For each indicator category, the cumulative sum of the values of tenders received in that financial year up to that point in time is calculated, and the range of values is normalized using min-max scaler. A final “government response” score is assigned using a modified z-score binning method (higher tender value = lower risk score):

Factor Cumulative Value	Factor Score
`response['sum'] <= mean`	5
`response['sum'] > mean & <= mean + std`	4
`response['sum'] > mean + std & <= mean + 2*std`	3
`response['sum'] > mean + 2std & <= mean + 3std`	2
`response['sum'] > mean + 3*std`	1

Final Flood Risk Score

The final flood risk score is calculated for each revenue circle / tehsil using a multi-criteria decision making method called TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution).

Before running the TOPSIS model, weights are assigned to each factor type based on its importance to the final risk score. The weightages are defined using the document “Disaster Risk and Resilience in India” drafted by the Ministry of Home Affairs and UNDP:

Factor	Hazard	Exposure	Vulnerability	Coping Capacity
Weightage	4	1	2	2

TOPSIS takes the individual factor scores and corresponding weightages as inputs. It works by defining an ideal solution and a non-ideal solution based on the factor scores, and computing the similarity of each individual RC’s factor score matrix to the ideal and non-ideal solution. The final risk score is generated by binning TOPSIS output into categories from 1-5 based on their relative scores.

Once the TOPSIS score is calculated at a revenue circle / tehsil level, the factor scores and final risk score are calculated at the district level by grouping revenue circles within each month by their corresponding districts, and calculating the arithmetic mean of the revenue circle scores for each factor.

The data model outputs a single CSV, which contains all the month-wise datapoints grouped by revenue circle. The district level scores are appended to the end of the CSV for ingestion into the platform backend.