Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Rahman, Muhammad Muhitur; Shafiullah, Md; Rahman, Syed Masiur; Khondaker, A.N.; Amao, Abduljamiu O.; Zahir, Md. Hasan

doi:10.3390/su12104045

Cited by 21 publications

(12 citation statements)

References 239 publications

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“…LASSO regression [7][8][9], Support Vector Machines (SVM) [10][11][12][13], Random Forest [14][15][16][17] and K-Nearest Neighbors (kNN) [18] models produce acceptable results to predict parameters of air quality. The analysis of the literature confirms that the mathematical models for air pollution forecasting show good quality of prognostic [19][20][21].…”

Section: Introductionmentioning

confidence: 72%

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Kujawska

Kulisz

Aubakirova

2022

J. Phys.: Conf. Ser.

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Air quality control and its prediction are particularly important for human health and life. Sulfur dioxide constitutes one of the air pollutants that play an important role in air quality pollution. An artificial neural network model was employed to forecast the levels of sulfur dioxide in the air of Zamość (Poland). The measured data of the meteorological station of Zamość in 2017-2019 were used for the model. Temperature (T), relative humidity (RH), wind speed (WS), wind direction (WD), SO2, PM10, NO2, NOx, CO, O3, C6H6 were used as input parameters for building the neural network model. Regression value (R) and Mean Squared Error (MSE) were used to estimation the model. The results show that neural network is capable of predicting the sulfur dioxide levels in the air.

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Section: Introductionmentioning

confidence: 72%

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Kujawska

Kulisz

Aubakirova

2022

J. Phys.: Conf. Ser.

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“…Sensors are embedded into the vehicles, cameras, and other devices installed in the city to collect environmental information that is processed to make informed decisions. IoT and Deep Learning techniques have also been employed to ensure air quality in cities [25]. Insurance companies nowadays place cameras and sensors in insured vehicles to lower insurance compensation rates [26].…”

Section: Iot Applicationsmentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

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“…The ANNs have been widely used in time series forecasting, including air pollution prediction and control [50][51][52][53]. In this work, we used four different neural models in the framework depicted in Figure 1: multilayer perceptron (MLP), radial basis function network (RBF), extreme learning machines (ELM), and echo state networks.…”

Section: Forecasting Models Used In the Proposed Approachmentioning

confidence: 99%

A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

et al. 2020

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Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities.

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Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Cited by 21 publications

References 239 publications

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

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