The Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine allows users to monitor, track, and evaluate water parameters in the Belize and Honduras Barrier Reefs from January 2013 to present using Landsat 8, Sentinel-2, and Aqua/Terra MODIS imagery.

Arduino code for pH, temperature monitoring, data logger, turbiostat, control LED for mimicking sun pattern. Python script for the simulation of microalgae growth with real solar profiles.

The project involves the approach of the Internet of Things (IoT) which will be done using a microcontroller. We are using customized sensor’s node for monitoring the parameters of water like pH, turbidity, temperature, oxidation-reduction potential, Dissolved Oxygen, industrial wastes, hospital waste etc.

projet de réseaux de capteurs, communication à distance, big data, communication full duplex

Estimación de turbidez en el agua a la entrada de la planta de tratamiento de SAMEEP, utilizando los productos Sentinel-2 MSI L2A y aprendizaje automático.

Open-Source Turbidity Meter and Mobile App

This Arduino UNO source code measures water turbidity in NTU using the SEN 109 turbidity sensor. It calibrates analog readings to digital values with the Kalman filter method, achieving over 90% accuracy. This setup provides reliable turbidity measurements for water quality monitoring.

Monitor water clarity and level with Arduino, LCD display, and buzzer alerts

Estimación de calidad de agua en el río Paraná para desarrollar un algoritmo mediante teledetección satelital

The Turbidity Monitoring System, a project to measures NTU in raw water using a microcontroller and a web-based interface. Installed at PDAM Tirta Bhagasasi, it provides real-time data and reporting, improving water quality assessment efficiency.

Code for 3rd year of the Conservation International and W&M Institute for Integrative Conservation collaborative Conservation Research Project concerning turbidity monitoring around Lake Alaotra, Madagascar.

💧Smart IoT-based system to monitor water quality in real-time using sensors and ML to detect purity and suggest improvements.