High-Level Project Summary
Aqua Decisions is a website that uses an interactive map to show recommendations and trends in geographic areas suitable for incorporating new urban rainwater harvesting facilities to supply rural localities that are difficult to access, helping to streamline the territorial planning process at different scales and with a sustainable and inclusive approach that reduces government investment costs and ensures access to drinking water while mitigating the environmental impact as the facilities are minimally invasive. In addition, it helps to democratise information through the reports section that citizens can use when a problem arises in the drinking water service.
Link to Final Project
Detailed Project Description
It is a web platform where users can make use of filters, bookmarks and searches by zones to obtain a report of suggestions, metrics and analysis of the most efficient locations to set up adequate infrastructure to provide solutions for water supply, such as water treatment and purification plants, water distribution networks, infrastructure for capturing and purifying rainwater, etc.
It also allows users to generate reports on problems they have in their areas related to water supply, which allows to generate more data and optimize the analysis model of the platform.
From various public and governmental databases, we store and use certain data from these databases as indicators such as average rainwater volume recorded over time, population density, temperature variation over time, slope, relief, etc. and train a prediction model as unsupervised learning and as a result it provides us with an indicator of the efficiency of the location analyzed, which we contrast with statistics and public policies.
The main benefits of using Aqua Decisions are as follows:
- Streamlines the territorial planning process at different scales.
- Provides timely information on drinking water issues and service deficiencies.
- Promotes the democratization of information from the citizenry's point of view.
- Provides predictions based on the current situation and historical events that favor decision making with a focus on environmental care.
We hope to provide drinking water service to communities located in areas of difficult access and to favor the inclusive development of planning with a focus on sustainability and inclusion processes.
We used technologies such as github to organise the team work, as well as to upload the progress of the project.
We created a website together with a sub-team, and it was divided into two parts:
- The front-end development programmed mainly in HTML, CSS, JS and using cross-platform libraries like Bootstrap.
- The back-end development we used Node JS, Express JS and Azure Web Services.
From Azure Machine Learning, we created a resource pool and designed a predictive model with Azure Machine Learning Studio.
In the near future we expect other variables to be added to provide a higher quality and thus predict where to place new health, recreation, security, administrative, public and many other facilities that will help cities improve their spatial planning processes.
Space Agency Data
The main sources used for the data were (earthdata.nasa.gov,2021) and (power.larc.nasa.gov,2021). This data helped to predict population growth in urban areas and at the same time determine where new drinking water facilities would be needed through rainwater harvesting.
Also, we create our own database using some filters to find data related to rainwater, urban areas, population growth, and more. The following is a breakdown of the information used:
- NASA (2021) Socioeconomic data and applications center (SEDAC). Recovered from: Downloads » Administrative Unit Center Points with Population Estimates, v4.11: Gridded Population of the World (GPW), v4 | SEDAC (columbia.edu) (02.10.2021)
- POWER (2021) Data Access viewer. Prediction of worldwide energy. Recovered from: POWER | Data Access Viewer (nasa.gov) (02.10.2021)
- NASA (2021) Earth Science Applied Sciences. Recovered from: ARSET - Understanding Phenology with Remote Sensing | NASA Applied Science (02.10.2021)
- NASA (2021) Worldview. Recovered from: EOSDIS Worldview (nasa.gov) (02.10.2021)
- NASA (2021) Earthdata. Recovered from: Giovanni (nasa.gov) (02.10.2021)
Hackathon Journey
It has certainly been an event that has tested our knowledge and skills in general. Above all, we consider it a positive process of great learning both in the technical part and in teamwork.
Fortunately, we all maintained good communication and knew how to communicate the relevant ideas at the right time, accepting the responsibilities and limitations that each one of us had, to ensure the best possible result.
We believe it has inspired us to continue growing, researching, practising and promoting participation in events of this style. From every point of view, each member was able to participate in both technical and design development.
We had to solve several problems on how to interconnect certain kind of services from the azure cloud to real time databases and how to communicate the resulting images or reports to the main page that aims to have clear and easy to understand information for any user.
There were certainly many more challenges to overcome than we imagined, but at the end of the day the experience has been very special and has reminded us that it is possible to think big by doing small actions today that add up together and allow us to shape the future we hope to live together.
References
- NASA (2021) Socioeconomic data and applications center (SEDAC). Recovered from: Downloads » Administrative Unit Center Points with Population Estimates, v4.11: Gridded Population of the World (GPW), v4 | SEDAC (columbia.edu) (02.10.2021)
- POWER (2021) Data Access viewer. Prediction of worldwide energy. Recovered from: POWER | Data Access Viewer (nasa.gov) (02.10.2021)
- NASA (2021) Earth Science Applied Sciences. Recovered from: ARSET - Understanding Phenology with Remote Sensing | NASA Applied Science (02.10.2021)
- NASA (2021) Worldview. Recovered from: EOSDIS Worldview (nasa.gov) (02.10.2021)
- NASA (2021) Earthdata. Recovered from: Giovanni (nasa.gov) (02.10.2021)
- Canva (2021) Recovered from: Inicio - Canva (02.10.2021)
- Google Drive (2021) Recovered from: Almacenamiento en la nube para casa y el trabajo - Google Drive (02.10.2021)
- ArcGIS (2021) Recovered from: Inicio de sesión de cuenta - ArcGIS Online (02.10.2021)
- Kepler (2021) Demo. Recovered from: kepler.gl (02.10.2021)
- GitHub (2021)
Tags
#water #populationgrowth #urbandevelopment #machinelearning
Global Judging
This project has been submitted for consideration during the Judging process.

