| Autor: |
Shashank Tolye, Vinayak Ashok Bharadi |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). |
| DOI: |
10.1109/cscita47329.2020.9137785 |
| Popis: |
The weather nowadays has become so unpredictable that there is a need for a system that predicts correct weather patterns and allows them to take a precautionary plan of action to cope up with it. Three major sources of providing very crucial weather data are IoT sensors, SAR data and social media posts from a particular location. In this paper, we are proposing a system that takes IoT sensor data, SAR images and twitter feeds from a geographical location and creates a learning model that will provide a decision-making system for anomaly detection in order to minimize or nullify any casualties. A temperature, pressure and humidity dataset of BME 280 sensor is processed with K-NN Classifiers and the results are presented here. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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