JolPulse: Mathematical Model, Units & Reproducible Benchmarks
Role & Domain Intent: Environmental scenario input/output lab (Synthetic flood exercises, not IoT, sensors, live warnings or response advice.)
Equations, Variables & Dimensional Rules
96 synthetic flood scenarios linking rainfall, river response and fictional response capacity.
Runoff_Proxy (mm) = Rain_24h (mm) × Runoff_Fraction(Soil_Saturation); Stage_Index(t) = Margin_0 − Trend (m/h) × t; Corridor_Route = argmin_{path} Σ Edge_Travel_Time subject to Edge_Open = true and Hub_Capacity ≥ People
Declared Units: 24-hour rainfall and runoff proxy in millimetres (mm); soil saturation in %; river stage margin in metres (m); stage trend in metres/hour (m/h); horizon in hours (6 h to 72 h); group size in persons.
Step-by-Step Numerical Verification
Case 'sunamganj-baseline-6' (Sunamganj Haor/Surma basin, 6-hour horizon, 120 people): 24h rainfall = 148.0 mm at 84.0% soil saturation yields illustrative runoff_fraction = 0.708 and runoff_proxy = 104.78 mm. With initial margin = 0.55 m and rise trend = 0.045 m/h, the capacity-aware shortest-path solver selects open fictional response corridors and generates bilingual (English/Bangla) exercise briefs.
| Scenario ID | Basin / Region | Rain 24h (mm) | Soil Sat. (%) | Runoff Proxy (mm) | Margin (m) | Trend (m/h) | Horizon (h) |
|---|---|---|---|---|---|---|---|
| sunamganj-baseline-6 | Sunamganj (Haor / Surma) | 148.0 | 84.0% | 104.78 | 0.55 | 0.045 | 6 |
| sylhet-surge-12 | Sylhet (Surma / Kushiyara) | 210.0 | 91.0% | 163.80 | 0.30 | 0.065 | 12 |
| kurigram-monsoon-24 | Kurigram (Brahmaputra) | 175.0 | 88.0% | 131.25 | 0.42 | 0.050 | 24 |
AsterVale Atlas — Governing Equations & Worked Verification
Canonical Module URL: https://khanio.co.technology/environmental-indicators/ · Launch Interactive Runtime ↗
Scope: Seven-indicator environmental comparison across synthetic study areas.
Boundary & Limitation: Illustrative indicators; not measured field evidence or certified environmental assessment.
NDVI = (NIR − Red) / (NIR + Red); Filtered Subset S = { r ∈ D : Date(r) ∈ [t_0, t_1] ∧ Area(r) = A ∧ Cloud_Pct(r) ≤ C_max }
Units & Conventions: Cloud cover threshold in %; spectral indices (NDVI, NDWI, EVI, etc.) dimensionless in [-1, 1]; observation counts in integer rows.
Worked Verification Example: Case 1 (Area = 'West Meadow', Indicator = NDVI, Max Cloud Cover = 35%): Sequential date → area → cloud filtering retains 179 valid synthetic observation rows, yielding sample Mean NDVI = 0.4534 and Median NDVI = 0.5182 with mutually exclusive exclusion counts.
| Case ID | Study Area | Indicator | Max Cloud (%) | Selected Rows | Mean Index | Median Index |
|---|---|---|---|---|---|---|
| Case 1 | West Meadow | NDVI | 35% | 179 | 0.4534 | 0.5182 |
| Case 2 | North Ridge | NDVI | 20% | 142 | 0.5120 | 0.5410 |
| Case 3 | Delta Marsh | NDWI | 35% | 168 | 0.3190 | 0.3345 |
Validation Status, Scope Exclusions & Disclaimers
- Data Status: Synthetic / precomputed browser demonstration fixtures generated deterministically from the project engine.
- Operational Limitation: Uncalibrated screening exercise; no live sensors, warnings or evacuation advice.
- Client-Side Execution: All interactive views, filters, and JSON/CSV exports execute locally in the user's browser with zero external API calls, zero telemetry, and no server-side persistence.
- Author & Principal Investigator: Mohammad Rezwan Khan, PhD (Aalborg University · BESS QA/QC & Power Systems Specialist).