ozone detection using deep learning

Past Projects - CS230 Deep Learning

Predicting Ground-Level Ozone Concetration from Urban Satellite and Street Level Imagery using Multimodal CNN by Andrea Vallebueno, Nicolas Suarez, Nina Prakash: report; Challenges in Scalable Distributed Training of Deep Neural Networks under communication constraints by Akshay Nalla, Kate Pistunova, Rajarshi Saha: report; Deep Learning for Physics Discovery by Danyal Mohaddes …

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Detection of anomalous episodes in urban Ozone maps ...

2020-09-08· Although outlier detection in deep learning has been usually undertaken by training the algorithm with categorical labels—classifier—, it can also be performed by using the algorithm as regressor. Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables about air quality. The measurements of these sensors can be treated individually—as time ...

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Detecting Abnormal Ozone Measurements With a Deep Learning ...

2018-07-02· The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Isère in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, restricted Boltzmann machines-based …

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Reliable detection of abnormal ozone measurements using an ...

This study aims to develop a deep learning-based approach that can properly detect ozone anomalies. Specifically, the proposed approach integrates a DBN modeling approach and one-class support vector machine (OCSVM). One benefit with the proposed detection system is that both advantages of the powerful feature extraction capability of DBNs and superior predicting capacity of OCSVM can be ...

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Machine Learning based Prediction System for Detecting Air ...

2019-09-16· PERFORMANCE ANALYSIS. Arnab Kumar Saha et al. [12] have used have used cloud based Air Pollution Monitoring Raspberry Pi controlled System. They measured Air Quality Index based on five criteria pollutants, such as particulate matter, ground level ozone, Sulphur Dioxide, Carbon Monoxide and Nitrogen Dioxide using Gas Detection Sensor or MQ135 Air Quality.

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Detecting abnormal ozone measurements with a deep learning ...

The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Is`ere in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, Restricted Boltzmann Machinesbased OCSVM and …

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7 Time Series Datasets for Machine Learning

2021-01-01· Ozone Level Detection Dataset. This dataset describes 6 years of ground ozone concentration observations and the objective is to predict whether it is an “ozone day” or not. The dataset contains 2,536 observations and 73 attributes. This is a classification prediction problem and the final attribute indicates the class value as “1” for an ozone day and “0” for a normal day. Two ...

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Using wavelet transform and dynamic time warping to ...

corrects ozone forecasts of the community multi-scale air quality (CMAQ) model for all monitoring stations in the EPA AirNow network. Even though the model significantly im- proved CMAQ forecasts, the bias-correction process and the unbalanced CMAQ modeling outputs are unclear. This paper discusses certain limitations of the machine learning model using wavelet transform and dynamic …

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Detecting Abnormal Ozone Measurements With a Deep Learning ...

2018-07-02· 2018-07-02· The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Isère in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, restricted Boltzmann …

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A real-time hourly ozone prediction system using deep ...

2019-06-08· This study uses a deep learning approach to forecast ozone concentrations over Seoul, South Korea for 2017. We use a deep convolutional neural network (CNN). We apply this method to predict the hourly ozone concentration on each day for the entire year using several predictors from the previous day, including the wind fields, temperature, relative humidity, pressure, and precipitation, …

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Face Mask Detection using Machine Learning and Deep Learning

possible using deep learning, and we just came up with that. Key Words: Machine Learning, Deep Learning, OpenCV, Tensorflow, Keras, MobileNetV2. 1. INTRODUCTION Corona Virus was originated in Wuhan, China at the end of 2019. Since then, it has been spreading like a wild fire in a forest. Millions have been affected and around 1,799,505[10] have unfortunately passed away thas on 30 of …

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Regional prediction of ground-level ozone using a hybrid ...

2020-04-20· 2020-04-20· Recently, several deep learning methods have achieved outstanding performance in general environmental prediction issues ... This phenomenon benefits the deep learning models to learn the pattern of ozone generating and dispersing processes, and to achieve better performance. Cross reference monitoring stations are located in remote areas, and the diurnal variation patterns of ozone ...

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Detecting abnormal ozone measurements with a deep learning ...

Detecting abnormal ozone measurements with a deep learning-based strategy Fouzi Harrou, Member, IEEE, Abdelkader Dairi, Ying Sun, Farid Kadri Abstract—Air quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems ...

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