Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction

Aarthi, Chelladurai; Ramya, Varatharaj Jeya; Falkowski-Gilski, Przemysław; Parameshachari, B. D.

doi:10.3390/su15021637

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…To avoid falling into the local optimum trap and the overfitting pitfall, Aarthi et al [19] present the Balanced (BSMO) method for efficient feature selection. The Pollution Control Board (CPCB) provided the predicted data for four cities in India.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Macherla,

Kotapati,

Sunitha

et al. 2023

ISI

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In many ways, our everyday lives depend on having access to reliable data about the state of the air around us. If you can predict the air quality ahead of time, you can put in place the right warnings and safety measures to keep people from getting hurt. The Control Boards in India set up the National Air Monitoring Programme (NAMP), which checks the air in 342 locations in 240 cities. There are a few distinct categories for the Air Quality Index (AQI). The AQI in Chennai was predicted using data that was collected and preprocessed to account for missing values and eliminate duplicates. Air quality forecasting using deep learning technology is investigated using a huge dataset describing the surrounding environment. This study suggests a scheme for classifying AQI values using multi-output and (MMS) based on long short-term memory (LSTM). Increased classification precision is achieved by using Chaotic Hunger Games Search (CHGS) in the hyper-parameter tuning process. When compared to conventional methods, the AQI value provided by the proposed deep learning model is more precise and accurate for a given location within a metropolis. The suggested deep learning algorithm improves forecast accuracy, serving as a public service announcement to bring levels down to a safe level. The AQI values can be reliably predicted by the deep learning method, which aids in sustainable urban development planning. By coordinating road traffic signals and encouraging people to take public transit in strategic areas, the AQI target value can lessen pollution.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Macherla,

Kotapati,

Sunitha

et al. 2023

ISI

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“…Aarthi et al [31] initially used a Min-Max normalization technique for filling in the missing attributes in the collected dataset, and then, the optimal attributes were selected from the preprocessed data by implementing a balanced spider monkey optimization (BSMO) algorithm. Based on the balancing factor, the BSMO algorithm selects the relevant attributes, which are given to the Bi-LSTM network for AQP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, a newer real-time dataset was acquired from the central pollution control board for four Indian cities: Cochin, Hyderabad, Chennai, and Bangalore. In this collected dataset (two times a week during a 24 h time period), the pollutants were monitored, and 104 observations were provided annually [31].…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Compared to this existing model, the proposed regression model (RSO and Bi-GRU) obtained better AQP with the RMSE of 9.82, MAE of 9.11, and SMAPE of 0.16 on a Beijing PM 2.5 dataset, as shown in Table 12. Aarthi et al [31] used the Min-Max normalization technique, BSMO, and Bi-LSTM network for AQP. The developed model proved to be effective in AQP with an MSE of 0.31, RMSE of 0.56, and MAE of 0.22 on a real-time dataset.…”

Section: Comparative Analysismentioning

confidence: 99%

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Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit

Gurumoorthy,

Kokku,

Falkowski-Gilski

et al. 2023

Sustainability

Self Cite

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In the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission of particulate matter (PM2.5) that affects human health conditions, especially in urban areas. In this research, a new optimization-based regression model was implemented for effective forecasting of air pollution. Firstly, the input data were acquired from a real-time Beijing PM2.5 dataset recorded from 1 January 2010 to 31 December 2014. Additionally, the newer real-time dataset was recorded from 2016 to 2022 for four Indian cities: Cochin, Hyderabad, Chennai, and Bangalore. Then, data normalization was accomplished using the Min-Max normalization technique, along with correlation analysis for selecting highly correlated variables (wind direction, temperature, dew point, wind speed, and historical PM2.5). Next, the important features from the highly correlated variables were selected by implementing an optimization algorithm named reinforced swarm optimization (RSO). Further, the selected optimal features were given to the bi-directional gated recurrent unit (Bi-GRU) model for effective AQP. The extensive numerical analysis shows that the proposed model obtained a mean absolute error (MAE) of 9.11 and 0.19 and a mean square error (MSE) of 2.82 and 0.26 on the Beijing PM2.5 dataset and a real-time dataset. On both datasets, the error rate of the proposed model was minimal compared to other regression models.

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“…Power systems with defects that cause damage produce their elements as an outcome of the overheating process (Kumar et al, 2018;Dhanamjayulu et al, 2019;Khare et al, 2020;Lal and Thankachan, 2021). Additionally, there are certain common problems in these power systems, such as harmonic distortion, voltage sag, transient, and spikes (Arjunagi and Patil, 2021;Parida et al, 2021;Ramasamy and Perumal, 2021;Aarthi et al, 2023). With the help of Spider Monkey optimization CNN, MLI tackles the aforementioned issues and provides a high output voltage.…”

Section: Introductionmentioning

confidence: 99%

Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach

Praveena,

Sathishkumar

2024

Front. Energy Res.

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In recent years, the power quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonics level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work, the Spider Monkey Optimization convolutional neural network (SM-CNN)-based 31-level multilevel inverter (MLI) is used. This method balances the reactive power demands and enhances real power in the grid-tied photovoltaic (PV) system. The maximum power point tracking (MPPT) algorithm depending on radial basis function neural networks (RBFNNs) is used to maximize PV power. For strengthening the voltage level of the PV and to generate higher DC voltage with a minimized switching loss, an integrated boost fly back converter (IBFC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the total harmonic distortion (THD) value to 2.45% with an improved power factor.

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Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction

Cited by 18 publications

References 39 publications

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit

Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach

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