A Hybrid Deep Learning-Based Framework for Analyzing Causes of Climate Change and Global Warming

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Vani Makula, Akhil Khare, L.K. Suresh Kumar

10.14445/23488549/IJECE-V12I11P116

How to Cite?

Vani Makula, Akhil Khare, L.K. Suresh Kumar, "A Hybrid Deep Learning-Based Framework for Analyzing Causes of Climate Change and Global Warming," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 11, pp. 178-200, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I11P116

Abstract:

A change in climatic conditions, monsoon cycles, and an increased number of natural disasters are changing the natural state of affairs, affecting the ecosystem, agricultural activities, and human wellbeing. The ever-increasing data on atmospheric pollutants, which is characterized by high volume, speed, and diversity, is a significant problem to analyze in the domain of big data. This paper presented the internal correlations between various air pollutants and their respective role in global climatic changes and Global Warming. We present a new model to demonstrate the same-named Probabilistic Learning Hybrid Sequential Machine Learning (PLHSML), which combines probabilistic Learning via the Learning-Based Gaussian Weighted Averaging (LBGWA) technique with a hybrid sequential deep learning architecture. PLHSML is created to record the temporal dynamics, measure the uncertainty, and discover cause-and-effect patterns using time-series climate data. In the proposed PLHSML model, LBGWA was used to estimate the global and climatic scenario. As it is estimated that the probability of estimation is computed under the model, and the forecasting is done based on the Sequential Machine Learning. An empirical study carried out by us indicates that the model is more effective not only in studying the interaction of complex pollutants, but also in estimating their effect on climate phenomena correctly. The experiment has proved that the predictive accuracy of PLHSML is extremely better than the conventional models in that it bears a Root Mean Square Error (RMSE) of 0.09 and a Mean Absolute Error (MAE) of 0.07. These findings show that PLHSML is an effective analytical tool in order to get to know and mitigate the factors that cause global Warming and climate change.

Keywords:

Deep Learning, Artificial Intelligence, Climate Change Analysis, Global Warming Analysis, Machine Learning.

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