Soil NOx Emission Prediction via Recurrent Neural Networks  

作　　者：Zhaoan Wang Shaoping Xiao Cheryl Reuben Qiyu Wang Jun Wang 

机构地区：[1]Department of Mechanical Engineering,Iowa Technology Institute,University of Iowa,Iowa City,IA 52242,USA [2]Department of Chemical and Biochemical Engineering,Iowa Technology Institute,University of Iowa,Iowa City,IA 52242,USA

出　　处：《Computers, Materials & Continua》2023年第10期285-297,共13页计算机、材料和连续体（英文）

基　　金：support from the University of Iowa Jumpstarting Tomorrow Community Feasibility Grants and OVPR Interdisciplinary Scholars Program for this study.Z.Wang and S.Xiao received support from the U.S.Department of Education(E.D.#P116S210005);Q.Wang and J.Wang acknowledge the support from NASA Atmospheric Composition Modeling and Analysis Program(ACMAP,Grant#:80NSSC19K0950).

摘　　要：This paper presents designing sequence-to-sequence recurrent neural network(RNN)architectures for a novel study to predict soil NOx emissions,driven by the imperative of understanding and mitigating environmental impact.The study utilizes data collected by the Environmental Protection Agency(EPA)to develop two distinct RNN predictive models:one built upon the long-short term memory(LSTM)and the other utilizing the gated recurrent unit(GRU).These models are fed with a combination of historical and anticipated air temperature,air moisture,and NOx emissions as inputs to forecast future NOx emissions.Both LSTM and GRU models can capture the intricate pulse patterns inherent in soil NOx emissions.Notably,the GRU model emerges as the superior performer,surpassing the LSTM model in predictive accuracy while demonstrating efficiency by necessitating less training time.Intriguingly,the investigation into varying input features reveals that relying solely on past NOx emissions as input yields satisfactory performance,highlighting the dominant influence of this factor.The study also delves into the impact of altering input series lengths and training data sizes,yielding insights into optimal configurations for enhanced model performance.Importantly,the findings promise to advance our grasp of soil NOx emission dynamics,with implications for environmental management strategies.Looking ahead,the anticipated availability of additional measurements is poised to bolster machine-learning model efficacy.Furthermore,the future study will explore physical-based RNNs,a promising avenue for deeper insights into soil NOx emission prediction.

关 键 词：Soil NOx emission long-short term memory gated recurrent unit sequence-to-sequence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]