scholarly journals Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

2021 ◽  
Vol 12 (1) ◽  
pp. 256
Author(s):  
Julio Alberto Ramírez-Montañez ◽  
Marco Antonio Aceves-Fernández ◽  
Jesús Carlos Pedraza-Ortega ◽  
Efrén Gorrostieta-Hurtado ◽  
Artemio Sotomayor-Olmedo
Keyword(s):  
Particulate Matter ◽  
Short Term Memory ◽  
Chaotic Behavior ◽  
Airborne Particulate Matter ◽  
Simulation Software ◽  
Short Term ◽  
Software Packages ◽  
Memory Network ◽  
Long Short Term Memory

Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM10 and PM2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics.

scholarly journals Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Wei Li ◽  
Dalin Wang ◽  
Wei Zhou ◽  
Yimeng Wang ◽  
Chao Shen
Keyword(s):  
Health Monitoring ◽  
Short Term Memory ◽  
Weather Radar ◽  
Real Case ◽  
Short Term ◽  
Term Memory ◽  
Monitoring Framework ◽  
Memory Network ◽  
Long Short Term Memory

The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.

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