Environmental Modeling and Assessment Using CFD and Data-Driven Tools
TL;DR
An integrated set of methodologies that applies both three-dimensional computational fluid dynamics (CFD) and cutting-edge machine learning (ML) and deep learning (DL) methodologies for the environmental thermal assessment is provided.
Abstract
Modeling the temporal and spatial dynamics of heat pollution within environmental systems is essential to accurately predict thermal pollution within the environment. Therefore, accurate predictions of thermal pollution dynamics require an understanding of the multiscale, nonlinear interactions that establish the mechanisms of thermal transport, dispersion, and ecological response. In this paper, we provide an integrated set of methodologies that applies both three-dimensional computational fluid dynamics (CFD) and cutting-edge machine learning (ML) and deep learning (DL) methodologies for the environmental thermal assessment. We assess the performance of six types of predictive ML models-linear regression, random forest, gradient boosting, multilayer neural networks (MNNs), support vector regression (SVRs), and long short-term memory (LSTM) networks-against benchmark datasets for predicting thermal plumes, predicting cooling tower dynamics, and forecasting river temperatures using CFD. Among these models, the LSTM networks performed by far the best for predicting thermal activities over time (R² = 0.95, RMSE = 1.5°C). Conversely, the best performing model for identifying spatial thermal patterns was gradient boosting.