Ai based weather monitoring and prediction
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
The Weather Monitoring System is a comprehensive platform designed to enhance real-time weather forecasting and environmental monitoring using IoT, machine learning, and cloud technologies. This innovative system integrates a suite of sensors to measure critical environmental parameters, including temperature, humidity, atmospheric pressure, gas levels, and light intensity. These sensors are connected to a central processing unit powered by a Raspberry Pi, ensuring efficient data collection and preprocessing to remove noise and inconsistencies.
Data is transmitted to the ThingSpeak cloud platform, enabling centralized storage, visualization, and real-time access. Machine learning models play a pivotal role in the system: the Random Forest algorithm is employed for binary classification, such as predicting rain occurrences, while the LSTM (Long Short-Term Memory) model analyzes sequential data to generate probabilistic weather forecasts. These predictive models are critical for identifying short-term and long-term weather trends with high accuracy.
The system's outputs are made accessible to users via an intuitive web application, which provides dynamic graphs, historical data visualization, and real-time weather insights. Additionally, a notification mechanism powered by a GSM module sends SMS alerts to users, ensuring timely dissemination of critical weather information, such as rain warnings. This feature is particularly beneficial for agriculture, disaster management, and other weather- dependent domains.
The Weather Monitoring System demonstrates scalability and adaptability by incorporating cloud-based storage and a modular design that allows for the integration of additional sensors and advanced predictive models. By combining IoT technology, machine learning, and cloud computing, this project addresses key challenges in modern weather monitoring, such as noise in sensor data, communication delays, and prediction reliability. With its focus on accuracy, user-friendliness, and real-time responsiveness, the system offers significant advancements in environmental monitoring and decision-making, empowering users to adapt effectively to changing weather conditions.