Artificial Intelligence benefits Smart Cities and Businesses with Predictive Analytics
The urbanization process attracts an increasing number of people and industries, which affects cities. Forecasts from the World Economic Forum predict that the world’s population will grow up to 10 billion by 2050, of which 70% will live in cities. However, already 92% of the global population lives in cities, that fail to meet the World Health Organization’s air quality guidelines. This circumstance raises the awareness of clean air in smart cities.
With predictive analytics, smart cities can generate cleaner air
Artificial Intelligence (AI) acts as catalysts leading to transformations in cities. This is achieved by AI replicating the human learning process in many ways: In the learning phase of AI, input data is merged with the known output which allows the algorithm to learn. In the following operational period, the system then starts to integrate new data.
AI is the promising technology which supports smart cities to master their prospective challenges. Technological progress and the OpenData availability are essential drivers in the current AI boom worldwide and enable the next wave of Intelligent Automation cross-functional and -industrial. The biggest problem with the use of AI is the amount of data required, its classification and data quality. However, OpenData enables to overcome these issues by providing large amounts of data for everyone to use, at any time and for free.
In short: OpenData is Big Data freely available, that enables us to turn this into Smart Data. Therefore, OpenData offers the possibility to compare data transnationally and derives recommendations for action from this. Moreover, for smart cities, the use of OpenData is beneficial because the use of historical data from OpenData processed by AI, makes predictions possible. The underlying data, for example satellite or traffic data, is collected via sensors checking the air quality. These collected data sets are needed and used for predictive analytics. Through this combination, the computer system learns to give predictions regarding the air quality in advance. Interestingly, the forecast results are better with the AI component than the results without the smart combination, because AI identifies patterns where human intelligence recognizes only chaos and can understand more complex data sets. Therefore, AI can provide high-resolution air quality predictions up to one kilometer and 3-10 days in advance. Hence, appropriate measures can be taken from the analysis to decrease emissions, for example traffic control.
Predictive analytics is not only beneficial for cities but also for businesses
One must note that the use case can differ – the framework conditions are changing, but the “heart” of predictive analytics remains the same and can be used in any sector and company. Accordingly, companies can use predictive analytics, in general, to plan and forecast their tasks over the following years more effectively and accurately. Consequently, a company can, for example, react and adjust its production according to the prediction. With predictive analytics, companies no longer need to react, but can now proactively adjust to circumstances and know in advance what will happen tomorrow. Therefore, being able to process a large amount of data in real time, AI is the perfect resource management tool in an ever-changing (business) world.
Conclusively, the current transformation of traditional industries is pushed by OpenData and AI. Hence, AI, especially predictive analytics in our case, provides new opportunities for improving the company’s processes, boosting their productivity and helping smart cities to manage their resources (i.e. air quality) effectively. With AI, smart cities can enhance the quality of the citizen’s lives by establishing intelligent solutions, making cities more efficient, intelligent and liveable