scholarly journals Air Pollution Prediction in Smart Cities by using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 1272-1279
Keyword(s):  
Air Pollution ◽  
Smart Cities ◽  
Time Series Prediction ◽  
Arima Model ◽  
Urban Air Pollution ◽  
Handling Time ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Air Contamination ◽  
Spatio Temporal

The urban air pollution has an immediate effect on man health specifically in developing and mechanical countries. It can cause health issues such as cancer, cardiovascular diseases and high mortality rates. Continuous checking of contamination empowers the metropolitans to dissect the present traffic circumstance of the city and take their decision accordingly. Existing exploration has utilized diverse AI apparatuses for pollution forecast; notwithstanding, relative examination of these methods is regularly required to have a superior comprehension of their handling time for numerous datasets. In this work, we look at forecasting the air contamination by dealing with parameters of three different gases like SO2 ,NO2 ,O3 .This process involves to pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW),LSTM,ARIMA Model for time series prediction, Kmeans, Support Vector Regression is then used to classify the spatio-temporal pollution data of different areas over a period of 10 years.

scholarly journals Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6019
Author(s):  
José Manuel Lozano Domínguez ◽  
Faroq Al-Tam ◽  
Tomás de J. Mateo Sanguino ◽  
Noélia Correia
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Fuzzy Classifier ◽  
Logistic Regression Models ◽  
The Road ◽  
Learning Agent ◽  
Machine Learning Models

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

scholarly journals Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

2020 ◽  
Vol 12 (4) ◽  
pp. 1-19
Author(s):  
Prathap Rudra Boppuru ◽  
Ramesha K.
Keyword(s):  
Data Mining ◽  
Predictive Analytics ◽  
Time Series Prediction ◽  
Arima Model ◽  
Estimation Method ◽  
Time Of Day ◽  
Crime Patterns ◽  
Model Time Series ◽  
Spatio Temporal ◽  
One Year

In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform.

Urban air pollution alert service for smart cities

2018 ◽  
Author(s):  
Sandro Rodriguez Garzon ◽  
Sebastian Walther ◽  
Shaoning Pang ◽  
Bersant Deva ◽  
Axel Küpper
Keyword(s):  
Air Pollution ◽  
Smart Cities ◽  
Urban Air Pollution ◽  
Urban Air

scholarly journals Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

2013 ◽  
Vol 03 (03) ◽  
pp. 126-135 ◽  
Author(s):  
Artemio Sotomayor-Olmedo ◽  
Marco A. Aceves-Fernández ◽  
Efrén Gorrostieta-Hurtado ◽  
Carlos Pedraza-Ortega ◽  
Juan M. Ramos-Arreguín ◽  
...  
Keyword(s):  
Air Pollution ◽  
Support Vector Machines ◽  
Mexico City ◽  
Urban Air Pollution ◽  
Support Vector ◽  
Urban Air ◽  
Vector Machines

scholarly journals Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Chi-Man Vong ◽  
Weng-Fai Ip ◽  
Pak-kin Wong ◽  
Jing-yi Yang
Keyword(s):  
Air Pollution ◽  
Support Vector Machines ◽  
Time Series Prediction ◽  
Warning System ◽  
Health Impact ◽  
Support Vector ◽  
Medical Practitioners ◽  
Vector Machines ◽  
Forecasting System ◽  
Short Term Prediction

Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

scholarly journals Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques

2021 ◽  
Vol 9 (12) ◽  
pp. 273-277
Author(s):  
Mrs. Gowri G
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Predictive Model ◽  
Regression Models ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Temporal Data ◽  
Spatio Temporal

Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models

scholarly journals Covariance matrix forecasting using support vector regression

2021 ◽  
Author(s):  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Covariance Matrix ◽  
Time Series Prediction ◽  
Dynamic Modelling ◽  
Positive Definiteness ◽  
Covariance Matrices ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Modelling And Forecasting

AbstractSupport vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it can describe the dynamic characteristics of financial processes and make more accurate forecasts than other machine learning techniques. The first main contribution of this paper is to propose a methodology for dynamic modelling and forecasting covariance matrices based on support vector regression using the Cholesky decomposition. The procedure is applied to range-based covariance matrices of returns, which are estimated on the basis of low and high prices. Such prices are most often available with closing prices for many financial series and contain more information about volatility and relationships between returns. The methodology guarantees the positive definiteness of the forecasted covariance matrices and is flexible, as it can be applied to different dependence patterns. The second contribution of the paper is to show with an example of the exchange rates from the forex market that the covariance matrix forecasts calculated using the proposed approach are more accurate than the forecasts from the benchmark dynamic conditional correlation model. The advantage of the suggested procedure is higher during turbulent periods, i.e., when forecasting is the most difficult and accurate forecasts matter most.

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