Comparative Analysis of a Machine Learning model for Water Quality forecasting of the Guayas River based on the Internet of Things

Abstract: The objective is to propose the design of an intelligent model of real-time data capture based on IoT for monitoring and visualization of monitoring of the environmental variables of the water of the Guayas River of a network, through a Machine Learning Model for water quality forecasting: to be able to carry out a study to determine the economic and technical impact of the case in a specific area of the Guayas River. Oriented on cases or study models of water quality or treatment; the design of the network formed by IoT Sensors, Communication Network, and Cloud; and the design of the Dashboard of prediction model in the quality of the water in stages to present the indicators according to the data obtained from the sensors. The initial cost of the model in implementation for data capture, transfer, prediction and presentation may be high, but the long-term benefits and advantages in data management are transcendental for making different decisions related to water quality and the environment in the Guayas River

Keywords: Internet of Things, Machine learning, Water Quality

DOI: 10.54941/ahfe1004492

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