Designing Air Environment Monitoring Station and Parameter Prediction Model Using LSTM Network

The paper focuses on solving two problems. Firstly, we design a sensor station module of the parameters (CO,NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>,O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>, PM<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf>, PM<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf>), which is real-time and can send data and store it on the IoT platform. The second is to predict environment parameters using deep learning networks for points that must be measured to predict future air quality. For the first problem, we consulted several open sources, and circulars on the AQI index to find the necessary air environment parameters and hardware suitable for the problem. However, there are many problems related to the sensor, specifically calibrating and finding the characteristic curve and evaluating the error of the characteristic graph for gas sensors (CO,NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>,O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>), the IoT platform problem stores data using an available platform that supports APIs to store and display data. For the second problem, we have researched and consulted using the long short-term memory networks (LSTM) network model to predict air environment parameters. As a result, the system sent data within four months without errors. Besides, the parameter prediction part has relatively high accuracy up to 98.44% when deploying to predict future datasets.