Eurasian Journal of Mathematical Theory and Computer Sciences 4-jild 5-son (2024) · 20–23-betlar

USING NEURAL NETWORKS FOR CLIMATE MODELING AND PREDICTION

Qonarbaev, David, Janibekov, Ilxambek, Saypnazarov, Ramazan

DOI: 10.5281/zenodo.11385597 · Manbada o'qish → · PDF (manba serverida)

Annotatsiya

Climate modeling and prediction play a crucial role in understanding and combating the effects of climate change. As the Earth's climate becomes increasingly complex and unpredictable, there is a growing need for advanced tools and technologies to accurately forecast future trends. One such innovative approach is the use of neural networks - a form of artificial intelligence that mimics the human brain's ability to learn and adapt. By harnessing the power of neural networks, researchers and scientists are exploring new possibilities for improving the accuracy and efficiency of climate modeling and prediction. This article will take into account the potential benefits of using neural networks in climate science, highlighting their capabilities, applications, challenges, and future directions.

Neural networks, climate prediction, data patterns, forecasting accuracy, challenges, data quality, model complexity, computational resources, explainable AI, automated machine learning, climate change.

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex to'liq matnni saqlamaydi, manbaga havola beradi.