This document summarizes a research paper that proposes a smart air pollution detector using an SVM classification model. It begins with an abstract that describes the need to control rising air pollution levels in developing countries like India. It then discusses particulate matter (PM) and its health risks when concentrated. The paper proposes to regularly check PM concentration levels using machine learning techniques. It reviews related work applying models like naive Bayes, SVM and regression to predict air quality. It then describes the existing systems' limitations and proposes a system that classifies PM2.5 levels using logistic regression and forecasts levels using an SVM model for improved accuracy. The paper analyzes the results and concludes machine learning can accurately predict future pollution levels to help people be aware and take action
This document describes research using genetic programming (GP) and artificial neural networks (ANN) to develop short-term air quality forecast models for Pune, India. 36 models were developed using daily average meteorological and pollutant concentration data from 2005-2008 to predict concentrations of SOx, NOx, and particulate matter one day in advance. The models were designed to be robust in situations where complete input data is unavailable. Performance of the GP and ANN models was evaluated based on correlation, error, and other statistical measures. The research found that the GP models generally performed better than the ANN models, especially in cases with incomplete data, and had the advantage of generating equation-based forecasts.
Analysis Of Air Pollutants Affecting The Air Quality Using ARIMAIRJET Journal
This document discusses analyzing air pollutants affecting air quality using the ARIMA time series model. It begins with an abstract describing the decreasing air quality due to factors like traffic and industry. It then discusses predicting and forecasting the Air Quality Index using time series models like ARIMA. The document reviews literature on previous studies analyzing air pollution data using techniques like neural networks and random forests. It describes preprocessing time series air pollution data to address missing values and assess stationarity before deploying the ARIMA model to make predictions.
ENVIRONMENTAL QUALITY PREDICTION AND ITS DEPLOYMENTIRJET Journal
This document presents research on using machine learning models to predict environmental quality by analyzing data from environmental sensors. The researchers implemented classification algorithms like decision trees, logistic regression, naive bayes, random forest and support vector machines to predict air quality using metrics like accuracy. They found that the decision tree algorithm achieved the highest accuracy of 99.8% among the other models. The proposed system aims to develop a more effective machine learning model for air quality prediction compared to existing image-based techniques, in order to help monitor pollution levels and protect public health.
A Deep Learning Based Air Quality PredictionDereck Downing
The document discusses using deep learning techniques to predict air quality. Specifically, it proposes using a Long Short-Term Memory (LSTM) model to predict hourly air quality index values. The LSTM model is trained on historical air quality and meteorological data. The proposed LSTM model is found to outperform existing models at predicting air quality, as measured by a lower root mean square error (RMSE) value for predictions. The document aims to develop techniques for accurately forecasting air quality to help address increasing air pollution issues.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem. The volume and complexity of the data have created the need to explore various machine learning models, however, those models have advantages and disadvantages when applied to regional air pollution analysis, furthermore, most environmental problems are global distribution problems. This research addressed spatio-temporal problem using decentralized computational technique named Online Scalable SVM Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these criteria can be improved using the proposed OSSELM. Special consideration is given to distributed ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple monitoring stations dispersed over a geographical location). Moreover, the experimental results demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
The document presents a new method called GA-KELM for predicting air quality index (AQI) values. It introduces extreme learning machines (ELM) and discusses their limitations. It then proposes using a genetic algorithm to optimize the number of hidden nodes, thresholds, and weights in a kernel extreme learning machine (KELM) model in order to improve prediction accuracy. Experimental results on real-world datasets show the GA-KELM method trains faster and more accurately predicts AQI values than other methods like CMAQ, SVM, and DBN-BP.
This document describes research using genetic programming (GP) and artificial neural networks (ANN) to develop short-term air quality forecast models for Pune, India. 36 models were developed using daily average meteorological and pollutant concentration data from 2005-2008 to predict concentrations of SOx, NOx, and particulate matter one day in advance. The models were designed to be robust in situations where complete input data is unavailable. Performance of the GP and ANN models was evaluated based on correlation, error, and other statistical measures. The research found that the GP models generally performed better than the ANN models, especially in cases with incomplete data, and had the advantage of generating equation-based forecasts.
Analysis Of Air Pollutants Affecting The Air Quality Using ARIMAIRJET Journal
This document discusses analyzing air pollutants affecting air quality using the ARIMA time series model. It begins with an abstract describing the decreasing air quality due to factors like traffic and industry. It then discusses predicting and forecasting the Air Quality Index using time series models like ARIMA. The document reviews literature on previous studies analyzing air pollution data using techniques like neural networks and random forests. It describes preprocessing time series air pollution data to address missing values and assess stationarity before deploying the ARIMA model to make predictions.
ENVIRONMENTAL QUALITY PREDICTION AND ITS DEPLOYMENTIRJET Journal
This document presents research on using machine learning models to predict environmental quality by analyzing data from environmental sensors. The researchers implemented classification algorithms like decision trees, logistic regression, naive bayes, random forest and support vector machines to predict air quality using metrics like accuracy. They found that the decision tree algorithm achieved the highest accuracy of 99.8% among the other models. The proposed system aims to develop a more effective machine learning model for air quality prediction compared to existing image-based techniques, in order to help monitor pollution levels and protect public health.
A Deep Learning Based Air Quality PredictionDereck Downing
The document discusses using deep learning techniques to predict air quality. Specifically, it proposes using a Long Short-Term Memory (LSTM) model to predict hourly air quality index values. The LSTM model is trained on historical air quality and meteorological data. The proposed LSTM model is found to outperform existing models at predicting air quality, as measured by a lower root mean square error (RMSE) value for predictions. The document aims to develop techniques for accurately forecasting air quality to help address increasing air pollution issues.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem. The volume and complexity of the data have created the need to explore various machine learning models, however, those models have advantages and disadvantages when applied to regional air pollution analysis, furthermore, most environmental problems are global distribution problems. This research addressed spatio-temporal problem using decentralized computational technique named Online Scalable SVM Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these criteria can be improved using the proposed OSSELM. Special consideration is given to distributed ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple monitoring stations dispersed over a geographical location). Moreover, the experimental results demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air pollution analysis in Auckland region.
ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR...IJDKP
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem.
The volume and complexity of the data have created the need to explore various machine learning models,
however, those models have advantages and disadvantages when applied to regional air pollution analysis,
furthermore, most environmental problems are global distribution problems. This research addressed
spatio-temporal problem using decentralized computational technique named Online Scalable SVM
Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis
includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these
criteria can be improved using the proposed OSSELM. Special consideration is given to distributed
ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple
monitoring stations dispersed over a geographical location). Moreover, the experimental results
demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air
pollution analysis in Auckland region.
The document presents a new method called GA-KELM for predicting air quality index (AQI) values. It introduces extreme learning machines (ELM) and discusses their limitations. It then proposes using a genetic algorithm to optimize the number of hidden nodes, thresholds, and weights in a kernel extreme learning machine (KELM) model in order to improve prediction accuracy. Experimental results on real-world datasets show the GA-KELM method trains faster and more accurately predicts AQI values than other methods like CMAQ, SVM, and DBN-BP.
This document provides an abstract for a thesis submitted to GITAM University in partial fulfillment of the requirements for a Doctor of Philosophy in Computer Science. The thesis proposes developing an intelligent framework for air pollution monitoring using Internet of Things and machine learning techniques. It discusses using various sensors and an Arduino board connected to a WiFi device to monitor air quality levels. The data would be displayed on a mobile app or LCD. Various deep learning models like LSTM, RNN, GRU and CNN are explored to accurately predict air quality index and determine the optimal model. The objectives of the research are outlined as developing forecasting models using weather data and sensor density to analyze large datasets and predict pollution levels in cities.
IRJET - Prediction of Air Pollutant Concentration using Deep LearningIRJET Journal
This document describes a study that used artificial neural networks to predict PM2.5 air pollutant concentration levels in Manali, Chennai, India. The study collected data on PM2.5 levels as well as factors like NO2, SO2, CO, relative humidity, and wind speed over a three year period. An artificial neural network model with feed-forward backpropagation was developed using this data, with 6 input nodes (for each pollutant/factor) and 1 output node (for PM2.5 levels). The model was trained on 70% of the data and tested on the remaining 15%, achieving a correlation coefficient between predicted and observed PM2.5 of 0.8. The study demonstrated that artificial neural
IRJET- Prediction of Fine-Grained Air Quality for Pollution ControlIRJET Journal
This document discusses predicting fine-grained air quality for pollution control using machine learning algorithms. It proposes using a random forest algorithm with air quality index data to build a model for predicting pollution levels. The model considers variables like particulate matter, nitrogen dioxide, and carbon dioxide collected over several hours and days from Bangalore, India. Data preprocessing techniques are applied before training the random forest model. The trained model can then be used to predict real-time pollution levels and provide information to help control air pollution. Evaluation shows the proposed random forest approach provides more accurate predictions than existing deep learning methods.
Prediction of Air Quality Index using Random Forest AlgorithmIRJET Journal
This document discusses using a random forest algorithm to predict air quality index (AQI) levels. It involves collecting historical data on air pollution parameters from monitoring stations. The data is preprocessed, relevant features are selected, and the data is split into training and test sets. A random forest model is trained on the training data and hyperparameters are tuned. Random forest is well-suited for this task as it can make accurate predictions while reducing overfitting by combining decision trees trained on different subsets of the data. The model performance is evaluated using error metrics to test how well it can predict AQI levels. Accurate AQI forecasting could help reduce the health impacts of air pollution.
This document presents a framework for visualizing air quality data. It involves collecting data on various air pollutants, preprocessing the data, using a machine learning model to predict an Air Quality Index, and visualizing the results. Specifically, it trains a random forest regression model on data from the Central Pollution Control Board to predict the AQI based on parameters like PM2.5, PM10, NO2, etc. It then implements the model to make real-time predictions using an API and stores the results in a database. These predictions are visualized on an interactive web page to show users the current air quality levels. The framework aims to help people monitor local air quality and help government organizations address areas with poor air quality.
Atmospheric Pollutant Concentration Prediction Based on KPCA BPijtsrd
PM2.5 prediction research has important significance for improving human health and atmospheric environmental quality, etc. This paper uses a model combining nuclear principal component analysis method and neural network to study the prediction problem of meteorological pollutant concentration, and compares the experimental results with the prediction results of the original neural network and the principal component analysis neural network. Based on the O3, CO, PM10, SO2, NO2 concentrations and parallel meteorological conditions data of Beijing from 2016 to 2020, the PM2.5 concentration was predicted. First, reduce the latitude of the data, and then use the KPCA BP neural network algorithm for training. The results show that the average absolute error, root mean square error and expected variance score of the combined model are relatively good, the generalization ability is strong, and the extreme value prediction is the best, which is better than that of the single model. Xin Lin | Bo Wang | Wenjing Ai "Atmospheric Pollutant Concentration Prediction Based on KPCA-BP" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51746.pdf Paper URL: https://www.ijtsrd.com/engineering/environment-engineering/51746/atmospheric-pollutant-concentration-prediction-based-on-kpcabp/xin-lin
IRJET - Intelligent Weather Forecasting using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to forecast weather intelligently. It proposes using multi-target regression and recurrent neural network (RNN) models trained on historical weather data from Bangalore to predict future weather conditions like temperature, humidity, and precipitation. The data is first preprocessed before being fed to the models. The models are evaluated to accurately predict weather in the short term to help people like farmers and commuters without relying on expensive equipment.
Climate Visibility Prediction Using Machine LearningIRJET Journal
The document describes a research project that aims to develop a machine learning model to predict visibility distance based on climatic indicators. The researchers collected a dataset containing measurements of variables like temperature, humidity, wind speed, and atmospheric pressure along with corresponding visibility distance readings. They plan to preprocess the data, select important features, and train regression algorithms like decision trees, XGBoost, and linear regression to predict visibility. Accurately predicting visibility can benefit various sectors by improving safety and decision making. The researchers hope to contribute to knowledge in this area and provide a useful tool for organizations.
Climate Visibility Prediction Using Machine LearningIRJET Journal
The document describes a research project that aims to develop a machine learning model to predict visibility distance based on climatic indicators. The researchers collected a dataset containing measurements of variables like temperature, humidity, wind speed, and atmospheric pressure along with corresponding visibility distance readings. They plan to preprocess the data, select important features, and train regression algorithms like decision trees, XGBoost, and linear regression to predict visibility. Accurately predicting visibility can benefit various sectors by improving safety and decision making. The researchers intend to evaluate model performance, tune hyperparameters, and ultimately deploy the optimal model for real-world visibility forecasting.
This document summarizes a time series analysis of air pollution data from Richmond, Virginia conducted using R. The analysis examined particulate matter (PM2.5 and PM10), lead, carbon monoxide, and ozone over 2010-2013. Correlation between pollutants was low. Univariate time series models like ARIMA were fitted to each pollutant and compared to 2013 data. ARIMA predicted PM2.5 and lead levels accurately but not other pollutants. The analysis aimed to apply methods from a Bulgarian air pollution study to a US city.
Air Pollution Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict air pollution levels. Sensors are used to collect data on air quality, smoke and dust levels. This data is fed into a KNN machine learning model for training and testing. The KNN model achieved 99.1% accuracy in predicting air quality levels based on the Air Quality Index. Machine learning is effective for analyzing large environmental datasets and making accurate pollution predictions to help monitor air quality and reduce health issues from air pollution.
Air Pollution Prediction via Differential Evolution Strategies with Random Fo...IRJET Journal
This document discusses using a hybrid machine learning technique combining differential evolution and random forest methods to predict air pollution levels. It analyzes data on various pollutants from two cities in India - Delhi and Patna. The proposed approach is experimentally validated to achieve better performance compared to independent classifiers and multi-label classifiers in terms of accuracy, area under the curve, success index and correlation. Differential evolution is used to initialize population and optimize candidate solutions. Random forest creates an ensemble of decision trees to make predictions. The hybrid method is tested on predicting carbon monoxide, nitrogen dioxide and benzene levels using data from a monitoring station in Delhi.
Assessment of Variation in Concentration of Air Pollutants Within Monitoring ...IRJET Journal
This document summarizes a study that assessed variation in air pollutant concentration across 12 monitoring stations in Mumbai, India. The study analyzed monthly pollution data from 2020-2021 for 7 pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, ozone) from the Central Pollution Control Board. Statistical analysis using ANOVA and Tukey's HSD post-hoc test identified pairs of stations without significant differences in pollutant concentrations across all 7 pollutants. This analysis could help identify redundant monitoring stations. The study also used logistic regression to predict air quality class based on factors like tree cover, population density, temperature, and more.
PPT.pdf internship demo on machine lerningMisbanausheen1
The document summarizes an internship project to predict air quality using linear regression. The intern collected air quality data on carbon monoxide and ozone levels from the World Weather Repository. Using Python and scikit-learn, a linear regression model was trained to predict ozone levels based on carbon monoxide levels. The model achieved a prediction score of [number]%. The intern concluded the project demonstrated the potential of machine learning techniques like linear regression for making accurate predictions to benefit various sectors.
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
1. The study analyzed the impact of COVID-19 lockdown measures on air quality in major Indian cities using machine learning techniques. Meteorological normalization was used to remove the effects of weather factors from pollutant concentration data.
2. Various deep learning models were used to forecast pollutant concentrations and compare observed concentrations during lockdown. This identified significant reductions in most pollutants during lockdown compared to business-as-usual forecasts.
3. A novel hybrid deep learning-cuckoo search approach was proposed and developed to more accurately forecast pollutant concentrations by optimizing deep learning hyperparameters. This approach can help policymakers develop long-term air quality strategies.
An Analytical Survey on Prediction of Air Quality Indexijtsrd
A drastic increase of modernization gives birth to many industries and automobiles, which intern becomes the very common reason for the environmental issues like Air and water pollutions. Air pollution is the immediate affecting factors in our life, which contaminates the air that we breathe to cause serious health hazards. So it is very important to predict the Air quality index for the future coming days so that proper prompt action can be taken by the concern authorities to curb the same. The air quality reading for the different gases can be collect through the physical sensors and these readings can be used to predict the future Air quality index. Machine learning is acting as the catalyst in this prediction scenario to predict the accurate Air quality index for the future instance. Most of the learning systems need a huge amount of the data for the learning purpose and it is not possible to provide this every time. So it is a need to predict the air quality index by using considerable less amount of past instance data, This paper mainly concentrates on analyzing the past work in prediction of air quality index using machine learning and try to evaluate their flaws and to estimate the new possible way of prediction using machine learning. Suraj Kapse | Akshay Kurumkar | Vighnesh Manthapurvar | Prof. Rajesh Tak "An Analytical Survey on Prediction of Air Quality Index" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28072.pdf Paper URL: https://www.ijtsrd.com/engineering/information-technology/28072/an-analytical-survey-on-prediction-of-air-quality-index/suraj-kapse
This study developed a new three-dimensional variational data assimilation (3DVAR) system to assimilate MODIS aerosol optical depth (AOD) observations. The system analyzes the 3D mass concentrations of 14 aerosol species in the WRF-Chem model as part of a one-step minimization procedure. Assimilating AOD observations from MODIS improved forecasts of AOD and surface PM10 concentrations compared to the control run without data assimilation, as shown through comparisons with AERONET and CALIPSO observations of a major dust storm over East Asia in March 2010.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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This document summarizes a time series analysis of air pollution data from Richmond, Virginia conducted using R. The analysis examined particulate matter (PM2.5 and PM10), lead, carbon monoxide, and ozone over 2010-2013. Correlation between pollutants was low. Univariate time series models like ARIMA were fitted to each pollutant and compared to 2013 data. ARIMA predicted PM2.5 and lead levels accurately but not other pollutants. The analysis aimed to apply methods from a Bulgarian air pollution study to a US city.
Air Pollution Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict air pollution levels. Sensors are used to collect data on air quality, smoke and dust levels. This data is fed into a KNN machine learning model for training and testing. The KNN model achieved 99.1% accuracy in predicting air quality levels based on the Air Quality Index. Machine learning is effective for analyzing large environmental datasets and making accurate pollution predictions to help monitor air quality and reduce health issues from air pollution.
Air Pollution Prediction via Differential Evolution Strategies with Random Fo...IRJET Journal
This document discusses using a hybrid machine learning technique combining differential evolution and random forest methods to predict air pollution levels. It analyzes data on various pollutants from two cities in India - Delhi and Patna. The proposed approach is experimentally validated to achieve better performance compared to independent classifiers and multi-label classifiers in terms of accuracy, area under the curve, success index and correlation. Differential evolution is used to initialize population and optimize candidate solutions. Random forest creates an ensemble of decision trees to make predictions. The hybrid method is tested on predicting carbon monoxide, nitrogen dioxide and benzene levels using data from a monitoring station in Delhi.
Assessment of Variation in Concentration of Air Pollutants Within Monitoring ...IRJET Journal
This document summarizes a study that assessed variation in air pollutant concentration across 12 monitoring stations in Mumbai, India. The study analyzed monthly pollution data from 2020-2021 for 7 pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, ozone) from the Central Pollution Control Board. Statistical analysis using ANOVA and Tukey's HSD post-hoc test identified pairs of stations without significant differences in pollutant concentrations across all 7 pollutants. This analysis could help identify redundant monitoring stations. The study also used logistic regression to predict air quality class based on factors like tree cover, population density, temperature, and more.
PPT.pdf internship demo on machine lerningMisbanausheen1
The document summarizes an internship project to predict air quality using linear regression. The intern collected air quality data on carbon monoxide and ozone levels from the World Weather Repository. Using Python and scikit-learn, a linear regression model was trained to predict ozone levels based on carbon monoxide levels. The model achieved a prediction score of [number]%. The intern concluded the project demonstrated the potential of machine learning techniques like linear regression for making accurate predictions to benefit various sectors.
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
1. The study analyzed the impact of COVID-19 lockdown measures on air quality in major Indian cities using machine learning techniques. Meteorological normalization was used to remove the effects of weather factors from pollutant concentration data.
2. Various deep learning models were used to forecast pollutant concentrations and compare observed concentrations during lockdown. This identified significant reductions in most pollutants during lockdown compared to business-as-usual forecasts.
3. A novel hybrid deep learning-cuckoo search approach was proposed and developed to more accurately forecast pollutant concentrations by optimizing deep learning hyperparameters. This approach can help policymakers develop long-term air quality strategies.
An Analytical Survey on Prediction of Air Quality Indexijtsrd
A drastic increase of modernization gives birth to many industries and automobiles, which intern becomes the very common reason for the environmental issues like Air and water pollutions. Air pollution is the immediate affecting factors in our life, which contaminates the air that we breathe to cause serious health hazards. So it is very important to predict the Air quality index for the future coming days so that proper prompt action can be taken by the concern authorities to curb the same. The air quality reading for the different gases can be collect through the physical sensors and these readings can be used to predict the future Air quality index. Machine learning is acting as the catalyst in this prediction scenario to predict the accurate Air quality index for the future instance. Most of the learning systems need a huge amount of the data for the learning purpose and it is not possible to provide this every time. So it is a need to predict the air quality index by using considerable less amount of past instance data, This paper mainly concentrates on analyzing the past work in prediction of air quality index using machine learning and try to evaluate their flaws and to estimate the new possible way of prediction using machine learning. Suraj Kapse | Akshay Kurumkar | Vighnesh Manthapurvar | Prof. Rajesh Tak "An Analytical Survey on Prediction of Air Quality Index" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28072.pdf Paper URL: https://www.ijtsrd.com/engineering/information-technology/28072/an-analytical-survey-on-prediction-of-air-quality-index/suraj-kapse
This study developed a new three-dimensional variational data assimilation (3DVAR) system to assimilate MODIS aerosol optical depth (AOD) observations. The system analyzes the 3D mass concentrations of 14 aerosol species in the WRF-Chem model as part of a one-step minimization procedure. Assimilating AOD observations from MODIS improved forecasts of AOD and surface PM10 concentrations compared to the control run without data assimilation, as shown through comparisons with AERONET and CALIPSO observations of a major dust storm over East Asia in March 2010.
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TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
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STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
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A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
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Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
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This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
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P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
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A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
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React based fullstack edtech web applicationIRJET Journal
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A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
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Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
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Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
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"Inter-Society Networking Panel GRSS/MTT-S/CIS
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IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
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Understanding Inductive Bias in Machine LearningSUTEJAS
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
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governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.