An IoT-Driven System for Real-Time Weather Data Generation and Short-Term Forecasting Using Machine Learning Approaches

DOI

10.2991/978-94-6463-884-4_70How to use a DOI?

Keywords

Machine Learning; Internet of Things (IoT); Weather Forecasting; Real-Time Data Acquisition; Short-Term Forecasting; Support Vector Regression (SVR)

Abstract

Weather forecasting plays a crucial role in people’s lives and provides important information for social and economic activities. Yet still poses major challenges due to the uncertain nature of climatic conditions and lack of regional data procuring. The Internet of Things (IoT) has significantly transformed and refined environmental monitoring by facilitating seamless real-time data acquisition and analysis. An IoT-based system is built to gather real-time regional data, while machine learning algorithms are used for precise forecasting. Advanced sensors like an anemometer, wind vane, rain gauge, DHT22, BMP-280, GYML8511, etc. have been integrated for monitoring the weather parameters like temperature, humidity, wind speed, wind direction, atmospheric pressure, and rainfall simultaneously. This IoT based system facilitates remote sensing and advanced sensors for gathering environmental data. The ESP32 micro-controller provides wireless connectivity which gathers real-time data from the sensors and stores them in a cloud database, through which the information can be retrieved from any location around the world. In this study, we proposed different machine learning algorithms like KNN, SVR, Decision Tree, and Ridge Regression to predict the short-term weather data and evaluate the performance of these algorithms based on MSE, RMSE, MAE, and R-squared. The integration of machine learning with IoT improves short-term weather prediction and extreme weather detection, offering significant potential for environmental assessment and forecasting applications.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ahmed Thawhid Sabit
AU  - Bishal Roy
AU  - Syed Abu Safwan
AU  - Rayhan Ahmed Opi
AU  - Md. Shahid Iqbal
PY  - 2025
DA  - 2025/11/18
TI  - An IoT-Driven System for Real-Time Weather Data Generation and Short-Term Forecasting Using Machine Learning Approaches
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 582
EP  - 589
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_70
DO  - 10.2991/978-94-6463-884-4_70
ID  - Sabit2025
ER  -