Catching the Smoke Before the Fire: How We Built Parali Alert

The idea, cause and execution behind Parali Alert.
Hridaan Tomar

Published Oct 11, 2026
Last modified Oct 11, 2026
Every October, the skies over North India turn into a toxic haze. Between harvesting rice (paddy) and sowing wheat, farmers face a brutal agronomic deadline: they have just 10 to 15 days to clear 6 to 8 tons of loose straw per hectare from their fields. With severe shortages of specialized clearing machinery at local Custom Hiring Centers, the cheapest and fastest way to clear the field is simply to light a match.
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The problem with our current monitoring systems is that they act like smoke detectors without fire extinguishers. Authorities rely heavily on thermal anomaly sensors from NASA satellites. But thermal sensors only trigger after the combustion has started. By the time a red dot appears on a government dashboard, the crop is already incinerated, the smoke is airborne, and enforcement teams are arriving hours too late.
Flipping the Script: Predicting the Fire Our project, Parali Alert, completely shifts this paradigm. Instead of reacting to fires that are already burning, we built a proactive, predictive intelligence engine that gives authorities a 36 to 48-hour countdown to intervene before a fire is lit.
Rather than handing district officers a messy, glowing heatmap and making them guess where to go, Parali Alert provides a mathematically ranked "Intervention Queue." It tells them exactly which five villages they need to send their limited Happy Seeder machines to today for the maximum environmental impact.
Looking Down from Orbit: How It Works To predict human behavior at a geographical scale, we had to fuse several streams of satellite and meteorological telemetry. Here is how we break it down:
NDVI (Normalized Difference Vegetation Index) → A mathematical formula applied to satellite images that measures how "green" and alive plants are → This allows us to track fields from space. The moment the NDVI score suddenly drops, we know a farmer has just harvested their standing crop.
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NDTI (Normalized Difference Tillage Index) → A spectral index that uses short-wave infrared light to detect dry, dead plant matter on bare dirt → This verifies that highly flammable, dry stubble has been left behind in the field, waiting to be burned.
AWS Serverless Architecture (Lambda & EventBridge) → A cloud computing model where code runs automatically on a schedule without us managing physical servers → This allows us to fetch and process heavy daily satellite data automatically at midnight while keeping our hosting costs to literally pennies a month.
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Why We Built It This Way When designing the risk engine, it was tempting to throw a blackbox deep learning model at the problem. We explicitly chose not to. Government dispatches require an auditable trail. If an Agricultural Development Officer is rerouting expensive machinery, they need to know why. Our model is 100% deterministic, weighing five physical parameters (like harvest recency, straw dryness, and machinery demand) into a transparent Priority Score.
Furthermore, downloading traditional satellite imagery chunks would crash a standard laptop and rack up massive AWS bills. We engineered our backend to use Cloud-Optimized GeoTIFFs (COGs) via HTTP Range Requests. This means instead of downloading a 500 MB image tile, our system surgically extracts only the 85 KB of pixels covering a specific village, cutting our bandwidth overhead by 99.98%.
The Real Innovation: Consequence Over Occurrence What makes Parali Alert truly different is that we measure consequence, not just probability. A fire burning in an isolated area is bad, but a fire blowing directly into a metropolis is a public health crisis.
We integrated Open-Meteo wind vector forecasts to simulate the downwind trajectory of the smoke. If a field is high-risk and the wind is blowing southeast toward densely populated corridors like Ludhiana or Delhi NCR, our algorithm multiplies the priority score by 1.35x. We prioritize the fires that will hurt the most people.
Results and Honest Limitations We didn't just build a flashy UI; we backtested our engine against historical Kharif seasons (2021–2023) using a strict temporal holdout to ensure zero future-data leakage.
The results were striking:
Lead Time: While traditional thermal alerts operate at a negative 4.2-hour delay (post-ignition), Parali Alert delivered a +37.2-hour pre-ignition lead time.
Accuracy: We achieved 74.1% precision in our top 10% queue—a 2.3x improvement over traditional baselines.
Impact: In our simulation, the system successfully identified and theoretically averted fires across 14,200 hectares of cropland.
However, the system is not without limitations. Heavy November cloud cover and smog can completely blind our primary optical satellites (Sentinel-2). To mitigate this, we had to build a fallback layer using Sentinel-1 SAR (Synthetic Aperture Radar), which shoots microwave pulses through the clouds to detect the physical absence of standing crops based on backscatter drops. Additionally, weather forecasting is inherently volatile; a sudden shift in boundary layer humidity can alter the flammability window, requiring our system to constantly re-calibrate hourly.
Parali Alert isn't about pointing fingers at farmers who are trapped by harsh economic deadlines. It’s about giving governments the exact, timely data they need to step in and offer a better alternative before the match is struck.
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