scholarly journals Air Quality Prediction Using Supervised Machine Learning Technique

2021 ◽  
Vol 23 (08) ◽  
pp. 62-69
Author(s):  
D. Joshna ◽  
◽  
K. Madhurya ◽  
K. Srividya ◽  
K. Ramamohanarao ◽  
...  
Keyword(s):  
Air Quality ◽  
Supervised Machine Learning ◽  
Quality Prediction ◽  
Machine Learning Technique ◽  
Great Opportunity ◽  
Air Contamination ◽  
Human Wellbeing ◽  
Air Quality Prediction ◽  
Air Quality Forecast ◽  
Learning Technique

Generally, air contamination alludes to the arrival of different contamination into the air which are compromising the human wellbeing and planet also. The air contamination is the major hazardous horrendous to humankind at any point confronted. It causes major harm to creatures, plants and so forth, if this continues proceeding, the individuals will confront major circumstances in the forthcoming years. The significant toxins are from the vehicle and enterprises. In this way, to forestall this issue significant areas need to foresee the air quality from transport and ventures .In existing undertaking there are numerous hindrances. The venture is tied in with assessing the PM2.5 fixation by planning a photo based strategy. In any case photographic technique isn’t the only one adequate to compute PM2.5 since it contains just one of the grouping of toxins furthermore, it ascertains just PM2.5 so there are some passing up a great opportunity of the significant toxins and the data required for controlling the contamination .So along these lines we proposed the AI procedures by UI of GUI application. In this numerous dataset can be joined from the diverse source to shape a summed up dataset and different AI calculations are used to get the outcomes with the most extreme precision. From looking at different AI calculations we can get the best precision result. Our assessment gives the thorough manual to affectability assessment of model boundaries concerning generally speaking execution in forecast of air great contaminations through exactness computation. Furthermore to examine and think about the presentation of AI calculations from the dataset with assessment of GUI based UI air quality forecast by credits.

scholarly journals Air Quality Prediction by Machine Learning

2021 ◽  
pp. 486-492
Author(s):  
Ritik Sharma ◽  
Gaurav Shilimkar ◽  
Shivam Pisal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Contamination Level ◽  
Quality Prediction ◽  
Air Contamination ◽  
The Public ◽  
The Past ◽  
Air Quality Prediction ◽  
Future Information

The air quality observing framework estimates different air toxins in different areas to keep up great air quality. It is the consuming issue in the current situation. Air is defiled by the appearance of risky gases into the environment from the enterprises, vehicular outflows, and so forth These days, air contamination has arrived at basic levels and the air contamination level in many significant urban areas has crossed the air quality list esteem as set by the public authority. It significantly affects the soundness of the human. With the headway in innovation of ML, it is currently conceivable to anticipate the poisons dependent on the past information. In this paper we are presenting a gadget that can proceed with that can take present poisons and with the assistance of past toxins, we are running a calculation dependent on the ML to anticipate the future information of contaminations. The detected information is saved inside the Excel sheet for additional assessment. These sensors are utilized on the Arduino Uno stage to gather the contamination information.

Urbanization of Weather Forecast, Air-Quality Prediction and Climate Scenarios - Project URBI PRAGENSI

2021 ◽  
Author(s):  
Tomas Halenka ◽  
Michal Belda ◽  
Peter Huszar ◽  
Jan Karlicky ◽  
Tereza Novakova
Keyword(s):  
Air Quality ◽  
Weather Prediction ◽  
Regional Climate ◽  
Weather Forecast ◽  
Quality Prediction ◽  
Climate Scenarios ◽  
Air Quality Prediction ◽  
Air Quality Forecast ◽  
The City ◽  
Very High

<p>The ratio of population living in cities is growing and this is especially true for the largest ones, megacities. However, even smaller cities like the City of Prague  (about 1.5 M) can suffer significantly and the night time temperature difference under summer heat wave can achieve more than 5°C. To assess the impact of cities and urban structures on weather, climate and air-quality, modelling approach is commonly used and the inclusion of urban parameterization in land-surface interactions is of primary importance to capture the urban effects properly. This is especially important when going to higher resolution, which is common trend in operational weather forecast, air-quality prediction as well as regional climate modeling. This represents the rapidly developing research, motivated by specific risks in urban environment, with strong impacts on vulnerable communities there, leading to the tools to assess properly impacts within the cities and the effectiveness of adaptation and mitigation options applied there by the city authorities. Under the action towards the Smart Cities and within the framework for developing adequate climate services, such supporting tools for decission making are inevitable. It is valid not only for extreme heat waves impact prediction, but as well in air-quality forecast and in long term perspective in connection to climate change impacts assessment. This provides the background for the project within Operational Program Prague - The Pole of Growth “Urbanization of weather forecast, air-quality prediction and climate scenarios for Prague”, shortly URBI PRAGENSI.</p><p> </p><p>There are four main tasks within the project. First, urbanization of weather forecast, i.e. involving and testing the urban parameterization scheme in the weather prediction model can provide in very high resolution localized weather prediction and especially under the heat wave condition it can well capture the temperature differences in the city center with respect to the remote areas. There are applications, which can use such localized prediction for planning and decision making on e.g. public services for some specific groups of population in risks. Further, air-quality forecast based on such urbanized weather condition forecast can benefit from better estimates of temperature for chemical reactions, mixing height for dispersion conditions etc. Third, urbanized scenarios of climate change can provide better description of future conditions in the city for adaptation and mitigation options, moreover, in connection to urban heat island urbanized regional climate model in very high resolution is good tool for estimates of efficiency  of potential adaptation or mitigation measures which might be applied by the city administration. Last, but not least, microscale simulations using LES methods are supposed to be used for selected local hot-spots to solve them.</p>

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

scholarly journals Accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) model on Intel Xeon Phi processors

2017 ◽  
Author(s):  
Hui Wang ◽  
Huansheng Chen ◽  
Qizhong Wu ◽  
Junming Lin ◽  
Xueshun Chen ◽  
...  
Keyword(s):  
Air Quality ◽  
Environmental Research ◽  
Quality Prediction ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Parallel Scalability ◽  
Air Quality Prediction ◽  
Air Quality Forecast ◽  
Parallel Mode ◽  
Intel Xeon

Abstract. The GNAQPMS model is the global version of the Nested Air Quality Prediction Modelling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present our work of porting and optimizing the GNAQPMS model on the second generation Intel Xeon Phi processor codename “Knights Landing” (KNL). Compared with the first generation Xeon Phi coprocessor, KNL introduced many new hardware features such as a bootable processor, high performance in-package memory and ISA compatibility with Intel Xeon processor. In particular, we described the five optimizations we applied to the key modules of GNAQPMS – CBM-Z gas chemistry, advection, convection and wet deposition. These optimizations work well on both the KNL 7250 processor as well as the Intel Xeon processor E5-2697 V4. They include: 1) updating the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP in emission, advection, convection and chemistry modules; 2) fully employ the 512-bit wide vector processing units (VPU) on the KNL platform; 3) reducing unnecessary memory access to improve caches efficiency; 4) reducing thread local storage (TLS) in CBM-Z gas phase chemistry module to improve its OpenMP performance; 5) changing global communication from interface-files writing/reading to using Message Passing Interface (MPI) functions to improve the performance and the parallel scalability. These optimizations improved GNAQPMS performance great. The same optimizations also work well for the Intel Xeon Broadwell processor, specifically, E5-2697v4. Compared with the baseline version of GNAQPMS, the optimized version is 3.34x faster on KNL and 2.39x faster on CPU. Furthermore, the optimized version on KNL runs at 26 % lower average power compare to CPU. Combining the performance and energy improvement, the KNL platform is 47% more efficient compare to the CPU platform. The optimizations also enables much further parallel scalability on both the CPU cluster and KNL cluster – scale to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 % and 42.2 %, respectively.

scholarly journals Prediction and Analysis of Pollutant using Supervised Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 50-54
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Prediction Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Air Quality Prediction

Air is the most essential natural resource for the survival of humans, animals, and plants on the planet. Air is polluted due to the burning of fuels, exhaust gases from factories and industries, and mining operations. Now, air pollution becomes the most dangerous pollution that humanity ever faced. This causes many health effects on humans like respiratory, lung, and skin diseases, which also causes effects on plants, and animals to survive. Hence, air quality prediction and evaluation as becoming an important research area. In this paper, a machine learning-based prediction model is constructed for air quality forecasting. This model will help us to find the major pollutant present in the location along with the causes and sources of that particular pollutant. Air Quality Index value for India is used to predict air quality. The data is collected from various places throughout India so that the collected data is preprocessed to recover from null values, missing values, and duplicate values. The dataset is trained and tested with various machine learning algorithms like Logistic Regression, Naïve Bayes Classification, Random Forest, Support Vector Machine, K Nearest Neighbor, and Decision Tree algorithm in order to find the performance measurement of the above-mentioned algorithms. From this, the prediction model is constructed using the Decision Tree algorithm to predict the air quality, because it provides the best and highest accuracy of 100%. The machine learning-based air quality prediction model helps India meteorological department in predicting the future of air quality, and its status and depends on that they can take action.

scholarly journals Air Quality Index Prediction using Linear Regression

2019 ◽  
Vol 8 (2) ◽  
pp. 4247-4252
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Linear Regression ◽  
Quality Index ◽  
Air Quality Index ◽  
Quality Prediction ◽  
Metropolitan Regions ◽  
Air Contamination ◽  
Air Quality Prediction ◽  
Natural Life

controlling and preserving the better air excellence has become one of the most indispensible events in numerous manufacturing plus metropolitan regions at present. The excellence of air is harmfully affecting payable to the various forms of contamination affected via the transportation, power, fuels expenditures, etc. The installation of destructive fumes is spawning the severe hazard for the class of natural life in developed metropolises. Through cumulative air contamination, we require implementing competent air excellence monitoring models which gathers the statistics about the absorption of air impurities and be responsible for calculation of air contamination in each zone. Hence, air excellence estimation plus calculation has come to be a significant study area. The superiority of air is exaggerated by multi-dimensional influences comprising place, time plus indeterminate parameters. The intention of this development is to examine the machine learning based methods for air quality prediction.

scholarly journals Air Quality Prediction based on Supervised Machine Learning Methods

2019 ◽  
Vol 8 (9S4) ◽  
pp. 206-212
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Quality Prediction ◽  
Validation Data ◽  
Air Quality Prediction ◽  
Learning Techniques

Generally, Air pollution alludes to the issue of toxins into the air that are harmful to human well being and the entire planet. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Subsequently, air quality assessment and prediction has turned into a significant research zone. The aim is to investigate machine learning based techniques for air quality prediction. The air quality dataset is preprocessed with respect to univariate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning/preparing. Then, air quality is predicted using supervised machine learning techniques like Logistic Regression, Random Forest, K-Nearest Neighbors, Decision Tree and Support Vector Machines. The performance of various machine learning algorithms is compared with respect to Precision, Recall and F1 Score. It is found that Decision Tree algorithm works well for predicting air quality. This application can help the meteorological Department in predicting air quality. In future, this work can be optimized by applying Artificial Intelligence techniques.

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