Environmental impact evaluation using smart real-time weather monitoring systems: a systematic review

The observation and direct influence in the sphere of weather phenomena are undeniable, as very intensive weather events like hurricanes and heat waves thus disrupt human activities, destroy infrastructure, and are dangerous to human health. Weather observations are essential for various sectors, including agriculture, transportation, aviation, and disaster management. This review explores the role of IoT, Arduino, machine learning, and deep learning in enhancing smart real-time weather monitoring systems. IoT enables seamless data collection from sensors; Arduino provides a flexible platform for system integration, while machine learning and deep learning analyze patterns in weather data, improving prediction accuracy and real-time responses. This review systematically analyzes studies on smart real-time weather monitoring systems, focusing on their integration with mobile applications. Studies were selected based on relevance to weather prediction technologies, environmental monitoring systems, and the use of IoT and machine learning algorithms. Key findings highlight the potential of these systems to improve forecasting accuracy and contribute to sustainability goals. Another critical review finding is how intelligent real-time weather-monitoring systems may integrate with mobile applications to enhance weather forecasting and monitoring. It will, therefore, align with the sustainable development goals set globally, as the systems will contribute to attaining SDG 13 through improved climate action through better prediction. Innovative systems identify patterns in the weather and predict them much more accurately using machine learning algorithms based on sensors’ data. Such solutions support SDG 11- Make cities resilient-and SDG 7-optimize renewable energy usage. Such technological and infrastructural innovations also contribute to SDG 9 and ensure a path toward sustainable development. In conclusion, this review highlights the groundbreaking integration of smart real-time weather monitoring systems, offering a unique perspective by systematically analyzing the latest technologies—such as IoT, machine learning, and deep learning—and their applications in weather prediction.