WATER QUALITY MONITORING SYSTEM

Machine Learning - Thesis Project
Project Overview
In our Senior Year in College, we developed an Integrated Mapping and monitoring of classifying Water Quality of the Laguna Lake in which I can describe it as "Real Time Water Quality Monitoring System". It is a mixture of Internet of Things (IoT) Hardware and Website for the monitoring of data.

Team Members:
- Gabriel T. Rivero
- Joshua Victor R. Ang

Adviser
- Jonalyn G. Ebron

Panels
- Dennis A. Martillano
- Leonnel D. De Mesa
My Contributions
We used Amazon Web Services as our Server along with Ubuntu as our platform when monitoring our website. We also used a third-party dashboard called "Beebotte", which is is a cloud platform that provides key building blocks to accelerate the development of Internet of Things and real time connected applications. Beebotte enables the transformation of any physical object or software application into a channel of digital resources.

Beebotte relieves application developers from infrastructure and connectivity burden by providing an API and key building blocks offering device management and connectivity, data collection, storage and intelligence, and, customized dashboards for data visualization. Beebotte supports REST, Websockets and MQTT to connect anything and everything in real time.​We developed our website through HTML and CSS in a simplistic and minimalistic approach to show direct and straightforward information that will not let it be unappealing to the users of our website.
The Real Time Water Quality Monitoring System was created in order to determine the water quality specifically for the three water quality parameters: Temperature, pH Level, and Fecal Coliform in the suburban areas within our hometown in the Philippines. The data we received from the Laguna Lake Development Authority showed that not much data has been collected within our location so, with my adviser and my groupmate, we developed an IoT Device that would be deployed on one of the rivers that is connected to the Laguna Lake.

The results that we got from our three sensors connected to the IoT Device going to our website, it shows that Fecal Coliform shows the status as the most dangerous parameter within hometown where we deployed our IoT Device in which we alerted the Laguna Lake Development Authority about it. However, we did not conduct further laboratory testing which is now tasked to the next future researchers from our thesis study.

I would like to thank Ma'am Jonalyn G. Ebron and my groupmate, Joshua, for the constant guidance support and not giving up on us on our thesis. To my panels, Sir Dennis and Sir Leo, for simplifying and helping us through your additional guidance and knowledge of our thesis. To Laguna Lake Development Authority, for their heartwarming and non-stop support as well to create a much healthier and better version of our water supply not just for our hometown but for the entire country as well.
WATER QUALITY MONITORING SYSTEM
Machine Learning - Thesis Project
August 2021 - June 2022
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