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Smart city article details
| Title | Weather Station Using Internet Of Things And Machine Learning Algorithms |
|---|---|
| ID_Doc | 61573 |
| Authors | Badoni P.; Kumar S.; Shrivastava U.; Wadhwa M.; Datta P. |
| Year | 2024 |
| Published | Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024 |
| DOI | http://dx.doi.org/10.1109/TIACOMP64125.2024.00012 |
| Abstract | Weather uncertainty poses significant challenges to industries, including construction, major events (e.g., Wimbledon, IPL), and public health (e.g., heat events) this paper addresses these challenges by a new solution that introduces an "automated weather forecast station". Leverages IoT) technology The system uses advanced forecasting techniques such as ARIMA (Auto Regressive Integrated Moving Average) and deep learning to provide real-time weather data These by continuously collecting data from resources controlling the environmental sensors that have been deployed within a specific area It meets objectives and then the collected data is processed and made available through a user-friendly interface. While the concept of IoT-based weather forecasting is not new, this research goes beyond traditional applications. It extends its utility to smart cities and diverse industries, making it a valuable resource for individuals, organizations and government departments. By addressing climate-related challenges first, the program ensures safety and resilience in the face of climate change. The paper highlights the potential of these new solutions to improve decision-making processes, reduce climate-related problems, and enhance overall quality of life in urban and industrial settings as the world faces climate uncertainty increasing emphasis. © 2024 IEEE. |
| Author Keywords | ARIMA; Convolutional Neural Networks; Deep learning; EDS (Exploratory Data Analysis); IoT (Internet of Things); Random Forest; Weather forecasting |
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