Mohemmed Sha ^1* Sam Emmanuel ² Bindhu A ³ Mohamed Mustaq ⁴

• ¹ College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
• ² Department of Computer Science, Nesamony Memorial Christian College, Marthandam, India
• ³ Department of Computer Science, Infant Jesus College of Arts and Science for Women, Mulagumoodu, India
• ⁴ Department of Information Technology, The New College, Chennai, India

On a global scale, climate change refers to persistent alterations in weather conditions and temperature patterns. These modifications have far-reaching implications across the world. GHGs (Greenhouse Gases) play a crucial role in driving climate change. Most of these emissions originate from human activities, particularly those contributing to releasing CO2 and CH4. In the conventional approach, identifying emissions involves recognizing and quantifying the sources and amounts of GHG released into the atmosphere. However, this manual identification method has limitations, including being time-consuming, relying on incomplete resources, prone to human error, and lacking scalability and coverage. To address these challenges, a technology-based system is necessary for effectively identifying GHG emissions. To achieve this, numerous traditional research efforts have applied DL (Deep Learning) technology to benefit from advantages such as automation, pattern recognition, adaptability, and scalability. Nonetheless, there are still certain factors that this approach falls short in, namely accuracy, speed, and the ability to handle larger datasets. To address the issue, the proposed method utilized the configuration of a gating mechanism incorporating fine-tuning shifts in the Bi-LSTM-GRU algorithm to predict GHG emissions in top-emitting countries. The PRIMAP-host dataset is used in the respective method comprising subsector data such as CO2, CH4, and N2O to attain this. In the presented model, Bi-LSTM is used to capture significant features, handle vanishing gradient problems, etc, because of its process in both directions. Conversely, it is limited by overfitting and long-term dependencies. GRU is used with Bi-LSTM to address the issue for the advantages of memory efficiency, handling long-term dependencies, rapid training process and minimizes the overfitting by infusion of GRU in the input layer of BiLSTM with tuning process in the BiLSTM. Here, the configuration of gates with fine-tuning shifts to improve the prediction performance. Moreover, the efficiency of the proposed method is calculated with performance metrics. In addition, internal and external comparisons are carried out to reveal the greater performance of the respective research.