Iot based accurate weather forecast at micro level
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Date
2025
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Volume Title
Publisher
NHCE
Abstract
The development of an Internet of Things (IoT)-based weather monitoring and forecasting system is presented in this study. The system integrates multiple sensors, such as the DHT11 for temperature and humidity, LDR module for Light Intensity, BMP280 for atmospheric pressure, and rain sensors, with an ESP8266 microcontroller. Solar power integration ensures sustainability and energy efficiency, making the system suitable for deployment in both rural and urban areas. Weather forecasting is a crucial component of environmental monitoring that supports informed decision-making in a variety of sectors, including agriculture, urban planning, and disaster management.
The system analyzes past data trends to produce weather predictions for the next five hours using machine learning techniques, namely the Random Forest model used in Google Colab. The effectiveness, accuracy, and viability of the suggested IoT-based system are assessed by contrasting it with conventional machine learning models used in weather forecasting. The IoT-integrated strategy bridges the gap between environmental sensing and predictive analytics by providing real-time data collecting and processing, whereas standalone ML models mostly rely on vast amounts of historical data and computational resources to provide reliable predictions. This integration guarantees that the forecasts are updated constantly as new data becomes available and improves forecast precision.
Key findings show that the IoT-based system offers a sustainable, scalable, and affordable solution for micro-level weather forecasting, tackling important issues including energy dependence, data accessibility, and adaptation to different geographic situations. While the modular design makes it simple to replace with more sensors or cutting-edge components, the use of renewable energy sources, such as solar panels, guarantees continuous operation in distant areas.
The comparative analysis emphasizes the benefits of integrating machine learning with IoT over using separate ML models. Although machine learning models are excellent at evaluating large datasets and spotting intricate patterns, their high computing costs and dependence on previous data restrict their use in real-time. By combining real-time sensor data with predictive algorithms, the Internet of Things-based system gets over these restrictions and produces forecasts that are more precise and context-specific. Additionally, the cloud-based design of the system makes it easier to retrieve data and predictions remotely, improving scalability and usability for a variety of applications.
According to the study's findings, IoT-based weather forecasting systems enable data-driven decision-making at the micro level and constitute a paradigm change in environmental monitoring. Further developments, such the addition of edge computing for localized processing and sophisticated neural network models for enhanced predictive capabilities, are made possible by the combination of IoT and machine learning technology. Future studies might investigate these possibilities to improve system performance and broaden its scope of use, supporting international initiatives to address climate-related issues.