The researcher from the Universidad Internacional Iberoamericana (International Iberoamerican University) (UNIB), Juan Luis Vidal Mazón, collaborates in a research that proposes an artificial neural network to monitor the quality and predict the consumption of drinking water. He has also been joined by Dr. Carmen Lili Rodríguez, on behalf of the Universidad Europea del Atlántico (European University of the Atlantic) (UNEATLANTICO).
The research focuses on the classification of water quality and the prediction of water consumption. The Water Quality Index (WQI) and various parameters are used to achieve this. The proposal includes a deployment of an artificial neural network architecture that offers improved performance with reduced complexity.
To enhance the results, several machine learning models, such as Random Forest, Decision Tree, Support Vector Machine, among others, and deep learning models such as Convolutional Neural Network and Long Short-Term Memory, have been used to evaluate the performance of the proposal.
The objective of the research has been to develop a single high-accuracy model for predicting both water quality and water consumption, comparing the results with existing state-of-the-art approaches to validate performance.
The use of neural networks and other machine learning techniques has shown promise in the water field, enabling better resource management and more efficient decision-making to address water quality and scarcity challenges. The quality of water for industrial and personal uses requires different standards, and water scarcity is a pressing problem for many territories, which must be addressed responsibly and sustainably.
If you want to know more about this fascinating study, click here.
To read more research, check the UNIB repository.
Finally, the Universidad Internacional Iberoamericana (UNIB) offers the Master in Strategic Management with a Specialty in Information Technology. A program that provides the fundamentals and strategies to innovate and achieve the development of companies to face the competitiveness of new businesses.