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Corresponding Author

Hala H. Youssef

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees (ET). Deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are also included. Their performance was assessed using measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). There were differences in the efficacy of the models found in the study; some models showed more performance, particularly after optimization. The key results indicate that the optimized models with exceptional performance across several indicators are ET, LSTM, GRU, XGBoost, KNN, and RF with R2 values of 0.9617, 0.987, 0.6394, 0.912, 0.9998, 0.999853, 0.994777, 0.949155, and 0.98991, respectively for each dataset. Conversely, SVMs without optimization always perform worse. This Study underlines how important it is to choose and refine models to guarantee precise climate change predictions.

Keywords

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees (ET). Deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are also included. Their performance was assessed using measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). There were differences in the efficacy of the models found in the study; some models showed more performance, particularly after optimization. The key results indicate that the optimized models with exceptional performance across several indicators are ET, LSTM, GRU, XGBoost, KNN, and RF with R2 values of 0.9617, 0.987, 0.6394, 0.912, 0.9998, 0.999853, 0.994777, 0.949155, and 0.98991, respectively for each dataset. Conversely, SVMs without optimization always perform worse. This Study underlines how important it is to choose and refine models to guarantee precise climate change predictions.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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