Quito air quality modeling and prediction using meteorological and pollution data
This document describes the process of design, implementation and results of different functions of Machine Learning to use them objectively with the meteorological data of the municipality of Quito. Data that have been collected from 2004 to 2018 hourly by REMMAQ in nine stations around the distric...
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| フォーマット: | bachelorThesis |
| 言語: | spa |
| 出版事項: |
2019
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| オンライン･アクセス: | http://dspace.udla.edu.ec/handle/33000/11335 |
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タグ追加 | 要約: | This document describes the process of design, implementation and results of different functions of Machine Learning to use them objectively with the meteorological data of the municipality of Quito. Data that have been collected from 2004 to 2018 hourly by REMMAQ in nine stations around the district. It mainly focuses on linear regression algorithms, time series analysis and spatial interpolation analysis. During the definition of graphics to be implemented in the AirQ2 application, an analysis process was carried out on the contribution of each type of graph to obtain a better result when building prediction models on the air pollution in Quito. Each graph described here provides specific information and provides a guide to learn more about the different pollutants and stations of the data collected, as well as their contribution in the construction of prediction models. |
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