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JCSIS

Optimization of Geo-Information Systems Using Quantum Algorithms for Climate Change Adaptation Solutions

Sam M. K.Lima HongouSofia Arkhstan
Volume 4

Abstract

Climate change adaptation requires efficient decision-making tools capable of processing vast amounts of geographical and environmental data. This study explores the optimization of geoinformation systems (GIS) using quantum algorithms to enhance climate change adaptation strategies. Traditional GIS techniques face limitations in terms of data processing speed and complexity when analyzing large-scale environmental datasets. By leveraging quantum computing, this research proposes novel quantum algorithms that can significantly accelerate the optimization process of GIS models, improving their ability to predict and adapt to climate change impacts. Quantum-enhanced GIS systems utilize quantum machine learning and quantum algorithms for data analysis, pattern recognition, and simulation tasks that are essential for effective climate adaptation planning. The proposed solution aims to optimize resource allocation, land use, and disaster preparedness strategies in response to climate risks. The results highlight the potential of quantum algorithms to revolutionize the field of geo-information systems, providing more accurate, efficient, and scalable tools for climate change mitigation and adaptation.

Keywords

Geo-information systems, quantum algorithms, climate change adaptation, quantum computing, data optimization, environmental modeling

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