How To Tackle Air Quality Prediction Using Machine Learning?
Air pollution is a major concern in current times. According to WHO data, about 7 million people are at risk due to bad air quality. It is the cause of many diseases from asthma to skin infection, lung cancer to throat and eye problems. Air pollution is a threat to humankind and destructive to the environment. Harmful emissions from vehicles and industry are the prime cause for the greenhouse effect; amongst all, CO2 is the most significant contributor.
Machine Learning For Predicting Air Quality
Previously, statistical methods have been used to make estimations about air pollution. Due to the complexity and variation in data, these methods are not very helpful. However, over the last few decades, efforts have been being taken to address the issue of air quality through machine learning techniques.
Machine learning methods, such as adaptive boosting (AdaBoost), stacking ensemble, random forest, artificial neural network (ANN), stacking ensemble, and support vector machine (SVM), offer promising results for predicting air quality levels. Machine learning training in California emphasizes teaching the most useful machine learning techniques.
Challenges For Predicting Air Quality
Predicting air quality is a pretty challenging problem as the air quality of one area differs from another. In fact, even in one city, air quality can vary. For example, quiet residential areas will have different air quality than busy streets and industrial regions. Besides, several factors affect the number of pollutants in the air, such as temperature, rain, air pressure, etc. But the collected data offer numerous possibilities beyond studying the air quality. The predictive models are useful to take preventive actions for improving air quality.
Architecture Of Predicting Air Quality
The best machine learning training in California help learn the techniques to monitor the air quality. There are four layers in an air quality prediction architecture, and each layer will have a different function.
Data Gathering
In this layer, data will be collected from different heterogeneous devices linked in a smart city. The sensors deployed at various locations in the city will collect data about different pollutants such as nitrogen dioxide, Sulphur dioxide, particulate, etc. Once the data is collected, filtration and pre-processing will take place to remove the unnecessary information.
Communication
In this layer, the transferring of data to the other layers will take place. It comprises different communication technologies like 3G, 4G, Wi-Fi, LTE, etc. All the data transfer from the IoT devices to the data processing layer happens here. This layer is also useful for gateways that are capable of real-time processing. Using Fog computing can increase the latency rate.
Data Management / Storage
This layer is primarily responsible for the storage and analysis of data. As real-time processing is needed for the analysis, different third-party tools such as Spark, VoltDb, Storm can also be used here. The data is stored in the HDFS system, while other systems can be used for data query and analysis. Here, both in-memory and offline data analysis takes place. It is helpful in learning through different ML algorithms. Predictions and pattern findings also happen here.
Application
This layer is the interface for all the information as it is connected with real-time devices. Hence, the data and reports are transferred in the form of charts, dashboards to display. Mostly, the end-user of this information is the government agencies responsible for monitoring air quality. They use this data to make crucial decisions. It displays all the pollution-related information. By interacting with the pollution statistics, people can make decisions.
If you are keen to make a career in machine learning, choose SynergisticIT, the best online machine learning training in California. It will help you gain expertise in machine learning tools, techniques, and libraries to tackle real-life problems.
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