JAKEBOT: IoT Based and Machine Learning Water Quality Monitoring for Rivers.

Abstract

According to the data obtained in the analysis of the Conagua Water Treatment Program, in Mexico 70% of lakes, lagoons, rivers, and other water bodies present some type of contamination. This article proposes the design of a prototype for monitoring water quality in rivers based on Internet of Things (IoT) sensors and a predictive model Machine Learning, JAKEBOT. The main components include a microcontroller to process the system, a communication system for inter and intra communication, and pH, turbidity, and temperature sensors. The retrieved data are presented in a visual format on a cloud server for analysis applying a machine learning linear regression algorithm. If the acquired data is above the standard threshold, the model will present an alert in the analyzed time range. The system can monitor water quality automatically, so the components allow a highly mobile and low-cost system that helps environmental and government organizations in the generation of strategies and policies to raise awareness of water quality.