Authors

Mohammed Amjed Abdulrazik

Faculty of Computing and Information, Suez Canal University, Ismailia, Egypt

Hanan Hossuni Ali

Department of Electronics and Electrical Communications Engineering, Menoufia University, Egypt

Abstract

The aim of this research is to improve carbon sequestration through two innovative components: a Siamese network fusion model to predict atmospheric carbon levels and a methodology to identify optimal locations for tree planting. The first component involves the development of a sophisticated Siamese network fusion model that integrates multiple data sources to predict atmospheric carbon levels with high accuracy. This model leverages the unique capabilities of Siamese networks to learn similarity measures to efficiently fuse disparate data sets, thereby improving the reliability and accuracy of predictions. The second component addresses the urgent need for effective tree planting strategies by formulating a comprehensive methodology to identify optimal locations for afforestation. This method integrates environmental, climate and socio-economic factors to identify areas where tree planting would maximize carbon sequestration and ecological benefits. The integration of these two components aims to provide a robust framework for mitigating climate change through informed and strategic carbon sequestration initiatives.

Keywords

Climate change mitigation Ecological benefits tree planting optimization fusion model afforestation strategies Carbon sequestration atmospheric carbon prediction

Citation of this Article

Mohammed Amjed Abdulrazik, Hanan Hossuni Ali. (2025). Development of Low-Carbon Energy System by Tree Planting Optimization Using Machine Learning Techniques. International Current Journal of Engineering and Science - ICJES, 4(1), 1-9. Article DOI: https://doi.org/10.47001/ICJES/2025.401001

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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