ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Alfredo Raya Montaño
Universidad de Guadalajara
Jennifer López Chacón
Universidad Michoacana de San Nicolás de Hidalgo
0000-0001-5317-2496
Grecia Itzel Colín González
Universidad Michoacana de San Nicolás de Hidalgo
0009-0002-9530-296X
Juan Pablo Pérez Aguilar
Universidad Michoacana de San Nicolás de Hidalgo
0009-0001-9875-7567
Bertín Alonso López
Tecnológico Nacional de México
0009-0002-0|667-4324
Acerca de
This project aims to develop an innovative model based on neural networks and differential equations (DEs) to analyze, simulate, and predict the dynamics of pollutants in water bodies. The approach focuses on mixing problems, where the concentration of substances changes over time due to the inflow and outflow of pollutants within the system. Through mathematical modeling, the project seeks to accurately represent the physical and chemical processes affecting water quality, while neural networks provide the capability to learn complex patterns and make precise predictions based on observed data. The proposed methodology enables the identification of critical pollution peaks, predicts their evolution over time, and assesses their environmental impact, offering essential information for preventing and mitigating risks associated with water quality.
By integrating advanced artificial intelligence techniques with the rigor of mathematical modeling, this project addresses the growing need for innovative tools to support sustainable water management. Moreover, the developed model can serve as a key decision-making tool for environmental authorities and water resource managers. It facilitates strategic planning and the design of public policies aimed at ensuring the sustainability of natural resources. This approach not only promotes environmental conservation but also positively impacts economic and social development by securing access to clean water for present and future generations.
Referencias
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