## Inspiration 🐳

The inception of our platform was fueled by the growing water crises and the lack of accessible, real-time data on water quality. We recognized the urgent need for a tool that could offer immediate insights and predictive analyses on water quality. We aimed to bridge the gap between complex data and actionable insights, ensuring that every individual, community, and authority is equipped with precise information to make informed decisions.

## What it does❓

Our platform offers a dual solution of real-time water quality tracking and predictive analytics. It integrates data from 11 diverse sources, offering live, metric-based water quality indices. The predictive model, trained on a rich dataset of over 18,000 points, including 400 events, delivers 99.7% accurate predictions of water quality influenced by various parameters and events. Users can visualize these insights through intuitive heat maps and graphs, making the data accessible and actionable for a range of stakeholders, from concerned individuals and communities to governments and engineers.
We also developed an AR experience that allows users to interact with and visualize real time data points that the application provides, in addition to heat map layering to demonstrate the effectiveness and strength of the model.

## How we built it 🛠️

We harnessed the power of big data analytics and machine learning to construct our robust platform. The real-time tracking feature consolidates data from 11 different APIs, databases, and datasets, utilizing advanced algorithms to generate live water quality indices. The predictive model is a masterpiece of regression analysis, trained on a dataset enriched with 18,000 data points on >400 events, webscraped from three distinct big data sources. Our technology stack is scalable and versatile, ensuring accurate predictions and visualizations that empower users to monitor, plan, and act upon water quality data effectively.

## Challenges we ran into 😣

Collecting and consolidating a large enough dataset from numerous sources to attain unbiased information, finding sufficiently detailed 3D models, vectorizing the 1000s of text-based data points into meaningful vectors, hyperparameter optimization of the model to reduce errors to negligible amounts (1x10^-6 margin of error for values 1-10), and using the model's predictions and mathematical calculations to interpolate heat maps to accurately represent and visualize the data.

## Accomplishments that we're proud of 🔥

* A 99.7% accurate model that was self-trained on >18000 data points that we consolidated!
* Finding/scraping/consolidating data from turbidity indices & pH levels to social gatherings & future infrastructure projects!
* Providing intuitive, easily-understood visualizations of incredibly large and complex data sets!
* Using numerous GCP services ranging from compute, ML, satellite datasets, and more!

## What we learned 🤔

Blender, data sourcing, model optimization, and error handling were indubitably the greatest learning experiences for us over the course of these 36 hours!
## Inspiration

With the effects of climate change becoming more and more apparent, we wanted to make a tool that allows users to stay informed on current climate events and stay safe by being warned of nearby climate warnings.

## What it does

Our web app has two functions. One of the functions is to show a map of the entire world that displays markers on locations of current climate events like hurricanes, wildfires, etc. The other function allows users to submit their phone numbers to us, which subscribes the user to regular SMS updates through Twilio if there are any dangerous climate events in their vicinity. This SMS update is sent regardless of whether the user has the app open or not, allowing users to be sure that they will get the latest updates in case of any severe or dangerous weather patterns.

## How we built it

We used Angular to build our frontend. With that, we used the Google Maps API to show the world map along with markers, with information we got from our server. The server gets this climate data from the NASA EONET API. The server also uses Twilio along with Google Firebase to allow users to sign up and receive text message updates about severe climate events in their vicinity (within 50km).

## Challenges we ran into

For the front end, one of the biggest challenges was the markers on the map. Not only, did we need to place markers on many different climate event locations, but we wanted the markers to have different icons based on weather events. We also wanted to be able to filter the marker types for a better user experience. For the back end, we had challenges to figure out Twilio to be able to text users, Google firebase for user sign-in, and MongoDB for database operation. Using these tools was a challenge at first because this was our first time using these tools. We also ran into problems trying to accurately calculate a user's vicinity to current events due to the complex nature of geographical math, but after a lot of number crunching, and the use of a helpful library, we were accurately able to determine if any given event is within 50km of a users position based solely on the coordiantes.

## Accomplishments that we're proud of

We are really proud to make an app that not only informs users but can also help them in dangerous situations. We are also proud of ourselves for finding solutions to the tough technical challenges we ran into.

## What we learned

We learned how to use all the different tools that we used for the first time while making this project. We also refined our front-end and back-end experience and knowledge.

## What's next for Natural Event Tracker

We want to perhaps make the map run faster and have more features for the user, like more information, etc. We also are interested in finding more ways to help our users stay safer during future climate events that they may experience.
## Inspiration

An Article, about 86 per cent of Canada's plastic waste ends up in landfill, a big part due to Bad Sorting. We thought it shouldn't be impossible to build a prototype for a Smart bin.

## What it does

The Smart bin is able, using Object detection, to sort Plastic, Glass, Metal, and Paper

We see all around Canada the trash bins split into different types of trash. It sometimes becomes frustrating and this inspired us to built a solution that doesn't require us to think about the kind of trash being thrown

The Waste Wizard takes any kind of trash you want to throw, uses machine learning to detect what kind of bin it should be disposed in, and drops it in the proper disposal bin

## How we built it\

Using Recyclable Cardboard, used dc motors, and 3d printed parts.

## Challenges we ran into

We had to train our Model for the ground up, even getting all the data

## Accomplishments that we're proud of

We managed to get the whole infrastructure build and all the motor and sensors working.

## What we learned

How to create and train model, 3d print gears, use sensors

## What's next for Waste Wizard

A Smart bin able to sort the 7 types of plastic