Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado, Alexi; Ricardo, R.; Carbajal, Chiara

doi:10.14569/ijacsa.2020.0110443

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…LSTMs were also used in [37], where an LSTM-based, sequence-to-sequence architecture was proposed to handle the dynamic, spatial-temporal, and nonlinear characteristics of multivariate air-quality data. Similar studies, adopting LSTMs to model air pollution levels include [74][75][76][77][78], while in [79], LSTMs and GRUs were equally proposed. The problem of missing values in air-quality datasets was raised and subsequently tackled in [80], by designing an LSTM-based framework.…”

Section: Air Qualitymentioning

confidence: 99%

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

Papastefanopoulos,

Linardatos,

Panagiotakopoulos

et al. 2023

Smart Cities

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Smart cities are urban areas that utilize digital solutions to enhance the efficiency of conventional networks and services for sustainable growth, optimized resource management, and the well-being of its residents. Today, with the increase in urban populations worldwide, their importance is greater than ever before and, as a result, they are being rapidly developed to meet the varying needs of their inhabitants. The Internet of Things (IoT) lies at the heart of such efforts, as it allows for large amounts of data to be collected and subsequently used in intelligent ways that contribute to smart city goals. Time-series forecasting using deep learning has been a major research focus due to its significance in many real-world applications in key sectors, such as medicine, climate, retail, finance, and more. This review focuses on describing the most prominent deep learning time-series forecasting methods and their application to six smart city domains, and more specifically, on problems of a multivariate nature, where more than one IoT time series is involved.

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Section: Air Qualitymentioning

confidence: 99%

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

Papastefanopoulos,

Linardatos,

Panagiotakopoulos

et al. 2023

Smart Cities

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“…The last phase is data management, which analyses, processes, and stores data in the cloud. These data can be used to predict air quality [29].…”

Section: Introductionmentioning

confidence: 99%

Design of Smart IoT Device for Monitoring Short-term Exposure to Air Pollution Peaks

Nizeyimana¹,

Nsenga²,

Shibasaki³

et al. 2022

IJACSA

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Air pollution spikes have been causing harm to human beings and the environment. Most exposure to Air pollution spikes has demonstrated a significant impact on mental health, especially children at an early age. That lead to suicide or depression. Previous research concentrated on air pollution in general. Existing monitoring systems do not consider Short-term air pollution peaks. This paper presents the co-design of the hardware and software for IoT to monitor air pollution spikes for a short duration in real-time monitoring. The system comprises two technologies like edge computing to capture shortterm exposure and a mathematical model for distribution in analyzing the captured data. This system ensures the presence of the spikes start and end for each pollutant. Monte Carlo simulation has been used in this research to predict the next spike of each pollutant. Artificial Intelligent is used to analyze immutable data for a short term prediction. After the analysis, legislators based on intelligent contracts created using blockchain to reduce pollution based on its source.

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Analysis of deep learning approaches for air pollution prediction

Gugnani

Singh

2022

Multimed Tools Appl

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Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Cited by 3 publications

References 26 publications

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

Design of Smart IoT Device for Monitoring Short-term Exposure to Air Pollution Peaks

Analysis of deep learning approaches for air pollution prediction

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