Date of Award
2020-01-01
Degree Name
Master of Science
Department
Industrial Engineering
Advisor(s)
Jose F. Espiritu
Abstract
This study is focused to provide the insights of weather to understand the significance of weather changes in any parameter. Weather forecasting contributes to the social and economic welfare in many sections of the society. Weather is extremely difficult to predict because it is a complex and chaotic system. This means that small errors in the initial conditions of a forecast grow rapidly and affect predictability. Nowadays, massive real-time data is being generated by IoT devices, radars, weather stations, and satellites. The need to adopt big data analytics in IoT applications is compelling. These two technologies have already been recognized in the fields of IT and business. Data mining techniques and machine learning algorithms need to be considered and trained with big data to improve the accuracy of weather forecasts. The contribution to this problem is to analyze the accuracy and correlation between weather conditions with the use of different data mining and forecasting techniques to predict precipitation for the next year.
Language
en
Provenance
Received from ProQuest
Copyright Date
2020-08
File Size
109 pages
File Format
application/pdf
Rights Holder
Pedro Marquez
Recommended Citation
Marquez, Pedro, "Weather Prediction: Improving Accuracy Using Data Mining And Forecasting Techniques" (2020). Open Access Theses & Dissertations. 3104.
https://scholarworks.utep.edu/open_etd/3104