Abstract

Global warming, pollution, and poor air quality are some of the main environmental problems. Many people worldwide, including miners, oil field workers, sailors, and industrial workers, operate in high-stakes situations where it is essential to keep an eye on their surroundings. In order to prevent hazards or disasters, it becomes essential to routinely check meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity. Appropriate monitoring is required for maintaining a healthy society and achieve sustainable growth. The usage of Internet of Things (IoT) which has been connected in recent years to the development of new sensors and environment monitoring systems (EMS) encompasses many devices for monitoring results in real-time information about environmental factors like Air temperatures. IoT devices may gather a wide range of data points for environmental monitoring, including temperature, radiation levels, and contaminant levels, giving a complete picture of the planet’s condition. One of the most important factors for safe and efficient operations in pipelines, refineries, and oil fields is temperature forecasting. Monitoring systems employ sensors to understand environmental temperature variations and mitigating the impacts of climate changes including global warming must be of utmost importance with commitment to address its dreadful implications to ensure global sustenance. IoT usage which has had significant impact on raising environmental standards can be used to track weather changes. In this context, the goal of the current paper is to conduct a critical evaluation of significant contributions and research projects on deep learning, IoT data, and EMS. An EMS based on deep learning (DL) is proposed in this study; the proposed system is complex, accurate, efficient, economical, and reliable. In order to ensure worker safety throughout implementation, our effort focuses on making precise temperature projections using past data. Temperature Assessments using Deep Learning Technique (TADLT), the suggested schema, makes predictions with an accuracy of above 90% by utilizing deep learning principles.

Authors

R. Parameswari, D. Raj Balaji
Rathinam College of Arts and Science, India

Keywords

Environments, Temperature Reading, Internet of Things (IoT), Environment Monitoring Systems (EMS), Sensors, Sensor Data, Big Data, Oil Industries, Room Temperatures