Omarova, P.; Amirgaliyev, Y.; Kozbakova, A.; Ataniyazova, A. Application of Physics-Informed Neural Networks to River Silting Simulation. Appl. Sci.2023, 13, 11983.
Omarova, P.; Amirgaliyev, Y.; Kozbakova, A.; Ataniyazova, A. Application of Physics-Informed Neural Networks to River Silting Simulation. Appl. Sci. 2023, 13, 11983.
Omarova, P.; Amirgaliyev, Y.; Kozbakova, A.; Ataniyazova, A. Application of Physics-Informed Neural Networks to River Silting Simulation. Appl. Sci.2023, 13, 11983.
Omarova, P.; Amirgaliyev, Y.; Kozbakova, A.; Ataniyazova, A. Application of Physics-Informed Neural Networks to River Silting Simulation. Appl. Sci. 2023, 13, 11983.
Abstract
Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study's methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the Physics-informed neural networks (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANN) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived from the PINN method and those obtained through conventional numerical approaches using the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assumes paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations.
Keywords
Water pollution; artificial neural networks; CFD; Euler equation; PINN
Subject
Environmental and Earth Sciences, Space and Planetary Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
