Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

Amado, Timothy M.; Cruz, Jennifer C. Dela

doi:10.1109/tencon.2018.8650518

Cited by 44 publications

(12 citation statements)

References 9 publications

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“…The recent research contributions were the main focus of the study though a few important research studies, conducted and investigated in last two decades, were also included. The contributions were reported on various SEM methods used for several purposes, mainly air quality assessment [1,5,11,12,47,58,76,85,89,90]; water pollution monitoring methods [1,13,14,39,64,66,[71][72][73][91][92][93][94][95][96][97]; radiation monitoring methods [1,36]; and smart agriculture monitoring systems [1,14,28,54,60,62,63,[98][99][100][101][102].…”

Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

“…One such work was reported in [55], that was used to measure the leaf area index using SVM as the machine learning technique, with a Gaussian process model [56] and the accuracy of measurement found as 89% with a limited sample size also in this case. An expert system using AI has been implemented in [57] using the Naive Bayes [58] method and machine learning which operates on sensor data captured in agriculture. This work was useful in monitoring the quality of fertilizer, pesticides and the amount of water to be irrigated in the crops.…”

Section: Related Research and Studymentioning

confidence: 99%

“…Research on SAQM methods and systems have also been studied, and Table 4 presents a summary of different SAQM approaches used in recent literature on air quality monitoring systems. Air quality characterization [58] has been implemented using heterogenous sensors and machine learning methods. The monitoring as well as characterization of water quality was achieved but interoperability issues were reported in this work due to use of heterogenous sensors.…”

Section: Study Based On Smart Water Pollution Monitoring (Swpm) Systemsmentioning

confidence: 99%

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Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

323

109

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Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

Section: Related Research and Studymentioning

confidence: 99%

Section: Study Based On Smart Water Pollution Monitoring (Swpm) Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

323

109

View full text Add to dashboard Cite

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“…To measure the leaf index Hosseini et al propose a system with a Gaussian process model [91] and using SVM as ML method and reported 89% with a limited sample size [119]. To determine the level of fertilizer, pesticides, and water quantity used for plant irrigation, an expert system using AI was developed [78] using the Naive Bayes [17] method and studying ML using sensory data taken from agriculture. UAV is used [77] to investigated the crop quality tests [230] and soil health for phenological data of soybean crop [49].…”

Section: Challenges In Smart Environmentmentioning

confidence: 99%

Probabilistic data structures in smart city: Survey, applications, challenges, and research directions

Kumar

Singh

2022

AIS

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With the commencement of new technologies like IoT and the Cloud, the sources of data generation have increased exponentially. The use and processing of this generated data have motivated and given birth to many other domains. The concept of a smart city has also evolved from making use of this data in decision-making in the various aspects of daily life and also improvement in the traditional systems. In smart cities, various technologies work collaboratively; they include devices used for data collection, processing, storing, retrieval, analysis, and decision making. Big data storage, retrieval, and analysis play a vital role in smart city applications. Traditional data processing approaches face many challenges when dealing with such voluminous and high-speed generated data, such as semi-structured or unstructured data, data privacy, security, real-time responses, and so on. Probabilistic Data Structures (PDS) has been evolved as a potential solution for many applications in smart cities to complete this tedious task of handling big data with real-time response. PDS has been used in many smart city domains, including healthcare, transportation, the environment, energy, and industry. The goal of this paper is to provide a comprehensive review of PDS and its applications in the domains of smart cities. The prominent domain of the smart city has been explored in detail; origin, current research status, challenges, and existing application of PDS along with research gaps and future directions. The foremost aim of this paper is to provide a detailed survey of PDS in smart cities; for readers and researchers who want to explore this field; along with the research opportunities in the domains.

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“…With respect to prediction systems, artificial intelligence algorithms are widely used in smart city applications for classification prediction and regression prediction such as human activity classification [ 26 , 27 ], transportation [ 28 ], and air quality prediction [ 29 , 30 , 31 , 32 ]. In [ 33 ] the authors applied the ML algorithms to predict the air quality by using the data from 750 observations with 0.95 accuracy and their prediction was successful. [ 34 ] focused on predicting air pollution in Canada using an MLP, and the prediction model performed on PM 2.5 had 4.5 of MAE .…”

Section: Related Workmentioning

confidence: 99%

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

Kamarudin

Zakaria

Nishizaki

et al. 2021

Sensors

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This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

Cited by 44 publications

References 9 publications

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Probabilistic data structures in smart city: Survey, applications, challenges, and research directions

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

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