Impact of Programming Language on Air Quality Estimation

Year 2025, Volume: 20 Issue: 1, 80 - 87, 31.03.2025

Emre Dalkılıç , Şükrü Dursun

https://izlik.org/JA89CW85AS

Abstract

The world has started to gain extra awareness about human health and environmental health after the coronavirus outbreak. In parallel with the increasing environmental awareness, components such as the use of natural resources and the possibility of causing global environmental problems to have started to play an effective role in decision-making processes rather than the financial side of the projects that come to the agenda. States carry out various environmental policies through their ministries, such as preparing legislation on air quality protection and sources affecting air pollution, odor emissions, determining targets, principles, policies and strategies, determining, implementing and having implemented procedures, principles and criteria for the creation of air pollution maps and the preparation of clean air action plans. However, the current situation is no longer sufficient for policymaking, and it is necessary to foresee the future and take steps in this direction. Being able to see today through the eyes of tomorrow provides great convenience in combating problems before they reach the threshold of a crisis in military, political and economic terms as well as environmental terms. Machine learning, a sub-branch of computer science developed in the early 20th century from digital learning and pattern recognition studies in artificial intelligence, is a system that investigates the operability and writing of algorithms that can learn as a structural function and make predictions on data. Written algorithms are designed to learn, instead of following program instructions to the letter, to create data-based predictions from the inputs provided to the system and to act as a decision maker. In the future, there is a need for algorithms that can be written using programming languages to predict air pollution and to determine its effects on public health. Today, using machine learning methods to predict air pollution has become more popular with data and data processing capabilities, which are among the most invaluable capitals. In this study, studies on predictability of air pollution with programming languages will be presented.

Keywords

Programming Languages , Air Pollution , Forecast , Algorithm

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