Long-term Land Surface Temperature Forecasting in Different Climate Zones using Long Short-term Memory
DOI:
https://doi.org/10.47839/ijc.24.2.4006Keywords:
recurrent neural networks, long short-term memory, long-term temperature forecasting, temperature regime, climatic zones, land surface temperatureAbstract
Climate change, which has been observed for several decades, is becoming increasingly widespread. The consequences of such changes are a negative impact on ecosystems in different regions of the planet, as well as on the biosphere. The Sustainable Development Goals define climate action as one of the key goals, which covers a wide range of actions to avoid and mitigate the consequences caused by climate change. To preserve biodiversity, increase the safety of residents of cities and communities located in vulnerable regions, it is necessary to form an integrated approach that will ensure sustainable development and improve the quality of life. Understanding the future climate situation and potential consequences is impossible without high-quality forecasting of climate indicators. One of the main indicators is the temperature of the Earth’s surface. The article analyzes the use of a recurrent neural network with a long short-term memory for long-term forecasting of temperature in different climatic zones. A study of the forecast accuracy for different time periods, considering climatic zoning, was conducted. The results indicate the feasibility of using the proposed approach for forecasting future Earth surface temperatures based on historical data.
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