SR-SA v2 Algorithm

Simplified Risk–Score Aggregation · Contextual Water Risk Assessment

Algorithm Overview

SR-SA v2 (Simplified Risk–Score Aggregation, Version 2) is a deterministic, multi-parameter evaluation algorithm designed to assess water-related risks at the panchayat level. The algorithm integrates hydrological variability and land-use characteristics into a single composite risk score.

Instead of relying on prediction models, SR-SA v2 focuses on observed deviations, ensuring interpretability, transparency, and academic defensibility.

Input Parameters

🌧 Rainfall Current vs Normal Deviation
💧 Groundwater Change in Water Table
🌱 Land-use Urban & Forest Cover

Rainfall Deviation Calculation

Rainfall impact is computed using the percentage deviation between long-term normal rainfall and the current observed rainfall. This captures abnormal excess or deficit conditions.

Rainfall Deviation (%) = | Rnormal − Rcurrent | / Rnormal × 100

This normalized representation allows rainfall variability to be compared uniformly across regions.

Groundwater Fluctuation Assessment

Groundwater stress is evaluated using the absolute change between previous and current groundwater levels, normalized against a 3m reference maximum.

Groundwater Score = min( | GWlast − GWcurrent | / 3.0 × 100, 100 )

Result is scaled to 0–100. Larger fluctuations indicate increased vulnerability, particularly in water scarcity scenarios.

Land-use Influence

Land-use characteristics affect both surface runoff and groundwater recharge. Urban areas increase runoff, while forest cover improves infiltration. Both factors contribute equally to the land-use risk score.

Urban Component = ( Urban% / 100 ) × 50
Forest Deficit = ( (100 − Forest%) / 100 ) × 50
Land-use Score = min( Urban + Forest Deficit, 100 )

Result is always 0–100. A fully urbanized, deforested area scores 100; a fully forested rural area scores ~0.

Risk Score Aggregation

SR-SA v2 uses weighted aggregation, where parameter importance varies depending on the selected risk model.

Flood Risk Score = min( (0.4×R + 0.4×L + 0.2×G_rise) + Flood_Boost, 100 )

G_rise: Only counts if groundwater level rose (depth decreased).
Flood_Boost: Water_Body% × 1.2 (Lake proximity increases risk).

Water Scarcity Risk Score =
( 0.4×R + 0.4×G_fluct + 0.2×L ) × SWF

Surface Water Moderation Factor

Panchayats adjacent to significant water bodies (lakes, backwaters, reservoirs) have markedly reduced water scarcity risk.

SWF = max( 1.0 − ( Water_Body% / 50 ), 0.1 )
Range: [ 0.1, 1.0 ]

A panchayat with 35% water body coverage (e.g. Perumon) has SWF = 0.30, reducing scarcity by 70%.

Weight Distribution Visualization

Flood Model

Rainfall 40% · Land-use 40% · Groundwater 20%

Water Scarcity Model

Rainfall 40% · Groundwater 40% · Land-use 20%

Risk Level Classification

The final composite score (0–100) is mapped to qualitative risk levels to support interpretability.

Score < 30 → Low Risk
30 ≤ Score < 60 → Moderate Risk
Score ≥ 60 → High Risk

Sample Score Breakdown

Example: Rainfall Dev = 20%, GW change = 1.5m, Urban = 25%, Forest = 30%

20
Rainfall Score
50
Groundwater Score
47.5
Land-use Score

Flood Risk = (0.4 × 20) + (0.4 × 47.5) + (0.2 × 50)

= 8.0 + 19.0 + 10.0 = 37.0 → Moderate Risk

Key Characteristics of SR-SA v2

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