IoT Based Air Pollution Monitoring &amp; Prediction System

Rakib, Mohammed; Haq, Sanaulla; Hossain, Md. Ismail; Rahman, Tanzilur

doi:10.1109/iciset54810.2022.9775871

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…A threshold is set according to the number of observations, and thus, the null hypothesis that there is a unit root can be rejected (Dickey and Fuller, 1981). Since the null hypothesis was rejected, we are confident that all seven parameters are normal (Rakib et al, 2022). Therefore, temperature, humidity, TVOC, CO, CO2, CH2O, and PM do not require a difference and are in a steady state.…”

Section: Preprocessingmentioning

confidence: 97%

Unsupervised Outlier Detection for Time-Series Data of Indoor Air Quality Using LSTM Autoencoder with Ensemble Method

Park

Seo

Cho

2023

Preprint

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The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an autoencoder, to build a reconstruction error-based outlier detection model and extract latent features. The latent feature class-assisted vector machine detector constructs an additional outlier detection model using previously extracted latent features. Finally, the ensemble detector combines the two independent classifiers to define a new ensemble-based decision rule. Furthermore, because real-time anomaly detection proceeds with unsupervised learning, more stable and consistent external detection rules are defined than when using a single ensemble model. Laboratory tests with five random cases were performed for objective evaluation. Thus, we propose a framework that can be applied to various industrial environments by detecting and defining stable outlier decision rules.

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Section: Preprocessingmentioning

confidence: 97%

Unsupervised Outlier Detection for Time-Series Data of Indoor Air Quality Using LSTM Autoencoder with Ensemble Method

Park

Seo

Cho

2023

Preprint

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“…In addition, the system can log sensor data to a cloud server, allowing users to access data anywhere in the world in real-time. According to the study [23], the proposed approach incorporates a hardware component (a device with four sensors and a processing module) and a corresponding software model. An ARIMA model was employed in this analysis to predict the next day's AQI and the concentrations of harmful pollutants in the air the following day.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the values of level, trend and seasonal factors are updated using the α, β, and U coefficients. Finally, the AQI value at time t + k is predicted using the updated values of level, trend, and seasonal factors using equation (23).…”

Section: Problem Formulationmentioning

confidence: 99%

An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis

Ansari

Alam

2023

Arab J Sci Eng

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Air pollution is a significant environmental issue affecting public health and ecosystems worldwide, resulting from various sources such as industrial activities, vehicle emissions, and fossil fuel burning. Air pollution contributes to climate change and can cause several health problems, such as respiratory illnesses, cardiovascular disease, and cancer. A potential solution to this problem has been proposed by using different artificial intelligence (AI) and time-series models. These models are implemented in the cloud environment to forecast the Air Quality Index (AQI) utilizing Internet of things (IoT) devices. The recent influx of IoT-enabled time-series air pollution data poses challenges for traditional models. Various approaches have been explored to forecast AQI in the cloud environment using IoT devices. The primary objective of this study is to assess the efficacy of an IoT-Cloud-based model for forecasting the AQI under different meteorological conditions. To achieve this, we proposed a novel BO-HyTS approach that combines seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) and fine-tuned it by using Bayesian optimization to predict air pollution levels. The proposed BO-HyTS model can capture both linear and nonlinear characteristics of the time-series data, thus augmenting the accuracy of the forecasting process. Additionally, several AQI forecasting models, including classical time-series, machine learning, and deep learning, are employed to forecast air quality from time-series data. Five statistical evaluation metrics are incorporated to evaluate the effectiveness of models. While comparing the various algorithms among themselves becomes difficult, a non-parametric statistical significance test (Friedman test) is applied to assess the performance of the different machine learning, time-series, and deep learning models. The findings reveal that the proposed BO-HyTS model produced significantly better results than their competitor's, providing the most accurate and efficient forecasting model, with an MSE

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“…Due to increasing industrialization and urbanization, air pollution poses a serious threat to both the environment and human health, with air pollution claiming approximately 8 million lives each year. Air pollution not only affects mortality rates but also causes various health problems such as lung diseases, cardiovascular diseases, skin cancer, and other serious effects [1]. Additionally, air pollution increases the concentration of harmful pollutants such as ammonia, carbon monoxide, methane, hydrogen, nitrogen, and phosphorus, leading to climate change, acid rain, ozone depletion, and biodiversity loss.…”

Section: Introductionmentioning

confidence: 99%

“…All living things breathe in and breathe out air containing carbon dioxide and oxygen. Therefore, we need to maintain and monitor air quality [1]. The air present consists of several harmful gases that are harmful to the environment and public health [3].…”

Section: Introductionmentioning

confidence: 99%

Improve air quality tracking through a fully intelligent IoT-based program with AERO Virtual Assistant

Yanamadni,

Shesagiri,

Swathi

et al. 2024

MATEC Web Conf.

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Have you thought about the quality of the air we breathe? Living a healthy and happy life is so important in the wake of the COVID-19 pandemic. To live a healthy life, everyone needs to be aware of the quality of the air they breathe and how their actions impact current and future air quality. This project presents his IoT-based system for measuring, monitoring, communicating, and predicting future air quality. The system was developed using a combination of IoT and machine learning to monitor, analyze and predict air quality in real-time. Air quality can be measured using various gas sensors, temperature and humidity sensors. It also measures the number of hazardous gases emitted in industrial areas. Machine learning models are applied to data values from various sensors to predict future air quality. The proposed solution monitors the current air quality on the device through a small smart application, observes fluctuations using graphs, and sends SMS alerts if air quality parameters exceed acceptable values. can. Another important feature is the addition of AERO, a virtual assistant that allows anyone to communicate atmospheric details. This system allows businesses, organizations, industries, farmers, gardeners, healthcare workers, city dwellers, etc. to easily connect, communicate, and monitor the current air quality in their daily lives and lead healthier lives. You will be able to. This will allow you to perform the necessary actions.

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IoT Based Air Pollution Monitoring & Prediction System

Cited by 9 publications

References 17 publications

Unsupervised Outlier Detection for Time-Series Data of Indoor Air Quality Using LSTM Autoencoder with Ensemble Method

Unsupervised Outlier Detection for Time-Series Data of Indoor Air Quality Using LSTM Autoencoder with Ensemble Method

An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis

Improve air quality tracking through a fully intelligent IoT-based program with AERO Virtual Assistant

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