On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi, Ola; Serhan, Sami; Salah, Imad

doi:10.25046/aj050265

Cited by 11 publications

(3 citation statements)

References 53 publications

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“…In contrast, statistical regression like multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) and machine learning like nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGB), deep neural network (DNN), and support vector machine (SVM) can take the causalities of the predictors into account. To accommodate the time lags between the predictors and the outcome, deep learning enabled time‐series models, such as recurrent neural network (RNN), long‐short term memory (LSTM), and gate recurrent unit (GRU), have become very popular in recent years (Dai et al, 2021; Mao et al, 2021; Seng et al, 2021; Surakhi et al, 2020; Xayasouk et al, 2020; Zhao et al, 2018). Among them, MARS, RF, and XGB can prioritize the degrees of importance of the predictors and thus are suitable for feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective

Wang

Chang

2023

Journal of Forecasting

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Forecasting air quality index (AQI) is critically important to provide a basis for government policy makers, especially in public health, smart transportation, energy management, economic development, and sustainable environments. In reality, AQI consists of various components, such as PM2.5, PM10, CO, NO2, and SO2. Although numerous methods have been presented, few studies concurrently considered the causalities of socioeconomic indicators and meteorological factors and different data granularities. The aggregate AQI of Taiwan comprises five representative cities: Taipei, Hsinchu, Taichung, Tainan, and Kaohsiung. Research findings identify seasonal factors, carbon power generation, steel and metal production, highway cargo load, the number of registered cars, and retail and manufacturing employment population as the key indicators to predict the monthly AQI of Taiwan. For the daily AQI of Hsinchu and the hourly AQI of Kaohsiung, PM2.5, PM10, O3, ambient temperature, humidity, wind speed, wind direction, and pollutants (CO, NO2, and SO2) are recognized. Deep learning significantly outperforms machine learning in the hourly AQI while it performs slightly better in the daily AQI. With the presented framework, governments can balance the trade‐offs between economic development and environmental sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective

Wang

Chang

2023

Journal of Forecasting

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“…MLP has gained widespread popularity as a preferred choice among neural networks [29,30]. This is primarily attributed to its fast computational speed, straightforward implementation, and ability to achieve satisfactory performance with relatively smaller training datasets.…”

Section: Setup Of Proposed Modelmentioning

confidence: 99%

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

et al. 2023

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An intrusion detection system (IDS) plays a critical role in maintaining network security by continuously monitoring network traffic and host systems to detect any potential security breaches or suspicious activities. With the recent surge in cyberattacks, there is a growing need for automated and intelligent IDSs. Many of these systems are designed to learn the normal patterns of network traffic, enabling them to identify any deviations from the norm, which can be indicative of anomalous or malicious behavior. Machine learning methods have proven to be effective in detecting malicious payloads in network traffic. However, the increasing volume of data generated by IDSs poses significant security risks and emphasizes the need for stronger network security measures. The performance of traditional machine learning methods heavily relies on the dataset and its balanced distribution. Unfortunately, many IDS datasets suffer from imbalanced class distributions, which hampers the effectiveness of machine learning techniques and leads to missed detection and false alarms in conventional IDSs. To address this challenge, this paper proposes a novel model-based generative adversarial network (GAN) called TDCGAN, which aims to improve the detection rate of the minority class in imbalanced datasets while maintaining efficiency. The TDCGAN model comprises a generator and three discriminators, with an election layer incorporated at the end of the architecture. This allows for the selection of the optimal outcome from the discriminators’ outputs. The UGR’16 dataset is employed for evaluation and benchmarking purposes. Various machine learning algorithms are used for comparison to demonstrate the efficacy of the proposed TDCGAN model. Experimental results reveal that TDCGAN offers an effective solution for addressing imbalanced intrusion detection and outperforms other traditionally used oversampling techniques. By leveraging the power of GANs and incorporating an election layer, TDCGAN demonstrates superior performance in detecting security threats in imbalanced IDS datasets.

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“…LSTM used in this case is of two types, the stacked LSTM with multi hidden layers (three) and Bidirectional LSTM, which trains two LSTM on the input sequence. LSTM is a type of NN used for time-series forecasting problems [45].…”

Section: B Data-driven Model Setupmentioning

confidence: 99%

A Generalized Data-Driven Modelling for COVID-19 Pandemic Outbreak: Jordan Case Study

Salameh¹,

Surakhi²,

Alhaol³

et al. 2022

IJETAE

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The explosion of the COVID-19 pandemic shows the limitations of the existing healthcare system in handling health emergencies. Innovative technologies like artificial intelligence and mathematical modelling can play an essential role in effective planning operations for fighting coronavirus pandemic. In this paper, a comparison between the mathematical model performance for forecasting the number of positive cases in Jordan and three data-driven models is performed to provide more insight on the appropriate model that can be utilized for COVID-19 disease spreading analysis. Our finding shows that both types of models can assess disease-spreading research. Including more factors in the forecasting process can increase the complexity of mathematical model analysis. On the other hand, the datadriven models and mechanisms will be more appropriate for this purpose. However, much work is still needed to capture more factors in the modelling process and provide a reliable solution for stopping this pandemic

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On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Cited by 11 publications

References 53 publications

Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective

Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

A Generalized Data-Driven Modelling for COVID-19 Pandemic Outbreak: Jordan Case Study

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