Development of a Cloud-Linked Low-Cost Machine Learning-Enabled Weather Monitoring System
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Abstract
This research endeavor focuses on designing and developing a low-cost, Arduino-based weather monitoring system to measure and forecast real-time environmental data, viz. temperature, humidity, etc. The main goal is to provide an affordable and easy-to-use solution to forecast local weather more accurately. The proposed system uses sensors connected to an Arduino along with Wi-Fi technology, to send collected data to an online platform for real-time monitoring. The data were analyzed using a ML-based BiLSTM-Attention-IWOA forecast model for temperature and humidity prediction. Experimental results achieved a Mean Absolute Percentage Error (MAPE) of 5.82% for temperature prediction and 5.20% for humidity prediction, surpassing previous forecast tactics. This allows users to access weather information remotely using the internet. The results indicate that it maintains low cost and low power consumption. Besides, IoT helps to exhibit the data on mobile Apps. Machine Learning (ML)-based models help with better decision-making, disaster readiness, and ecological alertness through rigorous data analysis and predictions. Such system can provide attaining Sustainable Development Goals.
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Publication Details
- Type of Publication:
- Conference Name: IEEE Region 10 Symposium 2026
- Date of Conference: 29/06/2026 - 29/06/2026
- Venue: Penang, Malaysia
- Organizer: IEEE Region 10