RippleEffect AI — Build Smarter. Protect What Matters.

Inspiration

Every time a city expands, we make decisions that affect more than just the land we're building on.

Imagine a grassy area being replaced by a parking lot or an apartment complex. While the development might benefit the community, it also changes how rainwater interacts with the environment. Water that previously soaked into the soil now has fewer places to go, potentially increasing surface runoff and placing additional pressure on nearby drainage systems.

This made us think about a simple question:

What if we could understand the environmental consequences of development before construction even begins?

We didn't want to build another application that simply identifies environmental problems. We wanted to create something that helps people explore better alternatives.

That's how RippleEffect AI was born.

Our goal is to make urban development more informed, sustainable, and accessible by helping users visualize how construction decisions affect stormwater runoff, vegetation, and surrounding areas.

What It Does

RippleEffect AI is an interactive, evidence-aware urban planning platform that allows users to simulate development scenarios and understand their potential environmental impact.

Users can select a location, explore the surrounding landscape, and propose changes such as:

Building a parking lot or apartment complex.
Expanding existing roads.
Replacing vegetation with paved surfaces.
Introducing rain gardens and other green infrastructure.
Comparing alternative development locations.

The application analyzes how these changes affect estimated stormwater runoff and vegetation coverage.

1. Interactive Development Planning

Users can explore neighborhoods through an interactive map and visualize proposed changes directly on the landscape.

They can compare the existing environment with their proposed development, making it easier to understand how construction changes land use.

2. Stormwater Runoff Simulation

RippleEffect estimates how much additional runoff a development could generate during a selected rainfall event.

For example, replacing grass with asphalt generally reduces infiltration opportunities and increases surface runoff.

Our simulation estimates runoff volume using rainfall depth, land area, and land-cover-dependent runoff coefficients:

$$ V = \frac{P}{1000} \times A \times C $$

Where:

(V) is estimated runoff volume in cubic meters.
(P) is rainfall depth in millimeters.
(A) is land area in square meters.
(C) is the assumed runoff coefficient.

We also use terrain-based flow routing to visualize where modeled runoff may accumulate downhill.

3. Vegetation Impact Analysis

Beyond stormwater, RippleEffect estimates how much vegetated land could be affected by a proposed development.

This helps users compare the environmental trade-offs of different construction footprints rather than focusing exclusively on building placement.

4. AI-Powered Historical Flood Intelligence

One of the most important components of RippleEffect is our integration of Google Groundsource, a dataset introduced in 2026.

Groundsource uses AI to transform historical news reports into structured geographical flood-event records.

We integrated regional flood-history evidence into our application to provide additional context alongside the runoff simulation.

Users can explore historical flood-report regions, inspect available evidence, and understand why some locations may deserve additional investigation.

Importantly, we distinguish between historical flood reports and modeled runoff. Historical records provide context, but they do not establish that a particular property will flood.

5. Comparing Lower-Impact Development Alternatives

RippleEffect goes beyond showing potential consequences.

Users can compare alternative development footprints and explore stormwater-management strategies such as permeable pavement, rain gardens, and water-storage solutions.

The goal is to help users investigate ways to reduce runoff, preserve vegetation, and manage rainwater more responsibly.

Instead of asking whether we should stop development, RippleEffect asks how we can develop more responsibly.

How We Built It

We designed RippleEffect as a full-stack application combining interactive mapping, environmental modeling, geospatial analysis, and historical flood intelligence.

Frontend

We used React, JavaScript, Tailwind CSS, and Leaflet to build an interactive planning interface.

The application provides:

Satellite-based neighborhood visualization.
Development scenario selection.
Editable land-cover representations.
Rainfall controls.
Before-and-after comparisons.
Environmental impact metrics.
Historical flood-evidence visualization.
Development-alternative comparisons.

Our primary design goal was to make complex environmental information understandable without requiring users to have an engineering background.

Backend

We developed our backend using Python and FastAPI.

The backend processes development scenarios, performs environmental calculations, analyzes terrain-based flow patterns, and returns structured results to the frontend.

Environmental Simulation

We implemented a simplified runoff model that uses rainfall, land-cover characteristics, and surface area to estimate stormwater runoff.

We also incorporated elevation-based flow routing to represent how water may move through neighboring terrain cells.

These calculations provide comparative planning estimates rather than engineering-grade flood predictions.

Historical Flood Data

We incorporated Google's 2026 Groundsource dataset to connect historical flood evidence with our environmental analysis.

We processed regional records and integrated them into our geospatial workflow, allowing users to examine historical flood context while evaluating development scenarios.