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Air Quality Prediction using Machine Learning

Air Quality Prediction using Machine Learning

Price : 10000

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Course Duration
Approx 10

Course Price
₹ 10000

Course Level
High

Course Content

 

ABSTRACT

 

In the last several years, air pollution has risen steadily in urban environments. Cities like Gurugram, Faisalabad, Delhi, Beijing are few of the world’s most polluted cities and have seen a dangerous rise in air pollution levels. Forecasting is important because of the human, ecologic and economic toll of pollution, and is a useful investment at individual and community levels. Accurate forecasting will help us plan in advance, decreasing the effects on health and the costs associated. Local weather conditions strongly affect air pollution levels. Generating deterministic models to study air pollutant behavior in environmental science research is often not very accurate because they are complex and need simulation at the molecular interaction level. : Air pollution which is detrimental to people’s health is a wide spread problem across many countries around the world. Developing better air quality prediction approaches is an important research issue. Here comes machine learning to the rescue with high computing facilities to predict air pollution. We proposed a system using machine learning which predicts the air quality efficiently.

                                                                     

 

INTRODUCTION

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants, especially in fast developing countries like India and China. Air pollution is one of the main detriments to human health. According to World Health Organization, 7 million people are at health risk due to air pollution . It is a leading risk factor for majority of health problems like asthma, skin infections, heart issues, throat and eye diseases, bronchitis, lungs cancer and respiratory system’s diseases. Besides the health problems related to air pollution, it also poses a serious threat to our planet. Pollution emissions from the sources like vehicles and industry is the underlying cause of greenhouse effect, CO2 emissions are amongst the foremost contributors to the greenhouse phenomenon. Climate change has been widely discussed at the global forums and has remained a burning issue for the world since last two decades as a result of increased smog and ozone damage Exposure to air pollution can affect everyone, but it can be particularly harmful to people with a heart disease or a lung condition, elderly people and children. Studies show that long-term exposure to fine particulate air pollution or traffic-related air pollution is associated with environmental-cause mortality, even at concentration ranges well below the standard annual mean limit value.

Project Requirements

A. Hardware Requirement:-

      System             :         Pentium IV 2.4 GHz.    

      Hard Disk        :         500 GB.

      Ram                 :         4 GB.

      Any desktop / Laptop system with above configuration or higher level.

B. Software Requirements:-

      Operating system       :         Windows XP / 7

      Coding Language       :         Python

      Interpreter                  :         Python 3.6

         •      IDE                            :         Python IDE 

 

Conclusion

 

According to World Health Organization, 7 million people are at health risk due to air pollution. It is a leading risk factor for majority of health problems like asthma, skin infections, heart issues, throat and eye diseases, bronchitis, lungs cancer and respiratory system’s diseases. Besides the health problems related to air pollution, it also poses a serious threat to our planet. In order reduce this air pollution predicting the air quality in early stage accurately helps to take the better decisions to maintain the air quality. We proposed a system using machine learning which predicts the air quality efficiently and this helps to take the appropriate decisions in order to maintain the air quality.

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