Modeling air pollution by integrating ANFIS and metaheuristic algorithms

Yonar, Aynur; Yonar, Harun

doi:10.1007/s40808-022-01573-6

Cited by 26 publications

(10 citation statements)

References 37 publications

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“…The ANFIS model is susceptible to the problem of local minima during the training process, owing to the use of heuristic methods. To mitigate this issue and achieve the global optimal solution, researchers have increasingly turned to metaheuristic or evolutionary techniques such as particle swarm optimization (PSO) and genetic algorithms (GA) [24]. This study reaffirms that optimizing ANFIS with PSO and GA yields significantly improved accuracy, resulting in minimal disparities between predicted output values and the actual target output values.…”

Section: Anfis-gamentioning

confidence: 54%

“…Their simulated results showed that the ANFIS-PSO algorithm had the highest accuracy (99%), followed by the ANFIS-GA and ANFIS-BBO. Yomar et al [24] conducted a study on modeling air pollution by integrating ANFIS and three metaheuristic algorithms, which were GA, PSO, and DE (differential evolution). They compared the results of these methods to those of classical ANFIS and found that these methods were more successful than classical ANFIS for modeling and predicting air pollution.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

Kabengele,

Olayode,

Tartibu

2023

Applied Sciences

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The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.

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Section: Anfis-gamentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

Kabengele,

Olayode,

Tartibu

2023

Applied Sciences

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“…Meanwhile, environmental concerns are becoming more severe, endangering both nature and human health. The most serious threat is air pollution (Yonar and Yonar 2022 ). It is increasing due to a variety of factors, including city overcrowding, and industrial processes involving combustion.…”

Section: Introductionmentioning

confidence: 99%

“…It may be dangerous, particularly for people who suffer from respiratory illnesses. Each year, outdoor and indoor air pollution kills nearly 7 million people, according to the World Health Organization (WHO) (Yonar and Yonar 2022 ). Furthermore, the COVID-19 virus pandemic, which emerged in late 2019, highlighted the significance of the interaction between public health and the environment.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the COVID-19 virus pandemic, which emerged in late 2019, highlighted the significance of the interaction between public health and the environment. According to research studies, people exposed to long-term air pollution are more likely to contract and be harmed by viruses such as COVID-19 as a result of emerging chronic disorders (Ghorbani and Zamanifar 2022 ; Yonar and Yonar 2022 ). Therefore, it is crucial to continuously monitor the quality of the air in all indoor and outdoor areas, including homes, workplaces, and industrial facilities (Ghorbani and Zamanifar 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Fahim

Mhouti

Boudaa

et al. 2023

Model. Earth Syst. Environ.

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The automatic weather system serves to inform farmers, tourists, planners, and others with precise information to help them take the appropriate action. Today, with the advancement of smart technologies, the system has evolved into many sensing methods to gather real-time climate data. This article investigates the modeling and implementation of a low-cost weather station device that also functions to measure air quality. The proposed system based on the Internet of Things (IoT) allows access to real-time climate data for a given area. This system monitors environmental conditions such as ambient temperature, humidity, atmospheric pressure, altitude, and levels of harmful atmospheric gases like CO 2 and NO 2 . This real-time telemetry device uses MQ-135, DHT-11 and BMP280 sensors to gather data. The ESP32 board processes the obtained data from all sensors. Additionally, we present a model for a fuzzy inference system (FIS) that performs parameter categorization using a reasoning procedure and incorporates the results into an air quality index (AQI) that describes the levels of pollution for Al Hoceima city. The FIS takes CO 2 and NO 2 values as input and returns the AQI. The AQI for Al Hoceima city is categorized into six levels: Excellent , Good , Regular , Bad , Dangerous , and Very Dangerous . Furthermore, the suggested system's block hardware employs the Message Queuing Telemetry Transport (MQTT) protocol to broadcast collected data to a mobile and web application via the Internet. The suggested IoT-embedded device was tested in real life, and the results were promising.

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