Turn environmental data into early warnings, intelligent predictions, and actionable climate resilience.
Explore Risk MapRun Prediction▶ 30-second demoAI-generated environmental risk assessment.
High temperatures are increasing predicted water demand while rainfall remains below normal.
A virtual Gurugram-area city. Stress it with heat, rain, drought or falling groundwater, compare it with today, and see which neighbourhoods need help first.
Ranked by the highest of the Nexus, heat, flood and scarcity scores in this scenario.
Select a layer, then click a location.
Satellite-style scan of Gurugram: temperature, vegetation, building density and surface.
From monitoring to prediction.
Scenario-based projection from the risk model. It is not a weather forecast.
Change the climate and compare the baseline with the scenario for Nexus, heat, flood and water scarcity.
| Score | Now | Scenario | Change | Likely range |
AI Insight:
Prioritized actions for Gurugram, ranked by urgency, then benefit per cost. Benefits are model-estimated score reductions under stated assumptions.
| # | Action | Why it matters | Urgency | Est. cost | Estimated benefit |
|---|
Environmental Decision Agent, grounded in the model's output rather than a general chatbot.
Groundwater, reported leaks, tanker requests and shortages for Gurugram.
Number = all reports in that area. Click an area to select it.
SAMPLE reports are demo data. Submit real ones in the Alerts section below.
Sample series shown. Paste your own daily meter readings in litres (7 or more values).
Turn heavy rain into stored water and see what it does to flood risk. Includes a daily water budget.
AI Action Plan:
AI Recommendation:
Small actions × many people.
Compete with your sector. Every litre and every action counts.
One click. A decision-ready briefing for the selected location.
Warnings for Gurugram from the heat, water-scarcity and flood scores, with precautions you can tick off.
Reports are saved as COMMUNITY data and counted separately from sample data.
How reliable is this prediction, where does each number come from, and what is missing?
Where every indicator comes from, and what is real versus prototype.
Environmental data to dashboard.
Prototype ML model trained on simulated environmental stress data, designed to be replaceable with live environmental datasets. Heat and water-scarcity scores are formulas over the model inputs; the flood score is a rule-based runoff-versus-drainage index with modelled imperviousness and drainage. Groundwater uses CGWB figures where loaded. Scores and projections are scenario-based decision support, not official forecasts. Confidence is the share of 240 ensemble and input-uncertainty scenarios that agree on the risk level; it measures model consistency, not validated real-world accuracy.