Deep Learning Weather Forecasting Techniques: Literature Survey

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Techniques used to predict climate risks: a brief literature survey

  • Review Article
  • Published: 18 June 2023
  • Volume 118 , pages 925–951, ( 2023 )

Cite this article

  • Ruchika Nanwani   ORCID: orcid.org/0000-0002-0823-6830 1 ,
  • Md Mahmudul Hasan 1 &
  • Silvia Cirstea 1  

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The global economy and way of life will be impacted by the increase in heat that the Earth is experiencing daily. Storms, cyclones, droughts, floods, and fires are examples of natural disasters that can strike without warning and have devastating effects on living things. Not only will this have a negative impact on the commercial and industrial development of the global economy, but it could also result in fatalities. Overall, it would seriously affect the upkeep of the Earth's ecosystems. With the development of machine learning algorithms, it is essential for us to comprehend how to use the available climate expert systems and various systematic procedures that can predict critical climatic conditions in advance so that potential disasters can be anticipated, identified, and mitigated. This study analyses effective machine learning methods for forecasting the risk of adverse weather events, such as heavy rain, temperature rise, wind, and drought. A recent study found that using artificial intelligence in data processing can be highly successful in producing a potentially effective climate forecast. Natural climate-related occurrences occur with predictable regularity. However, several of them exhibit diverse behaviour within their intervals. Compared to other conventional ways, artificial intelligence outfitted with potent machine learning strategies has shown to be effective in anticipating catastrophic tragedies.

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literature survey on weather forecasting

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Nanwani, R., Hasan, M.M. & Cirstea, S. Techniques used to predict climate risks: a brief literature survey. Nat Hazards 118 , 925–951 (2023). https://doi.org/10.1007/s11069-023-06046-2

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Received : 30 October 2022

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Published : 18 June 2023

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DOI : https://doi.org/10.1007/s11069-023-06046-2

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Time Series Data Analysis for Forecasting – A Literature Review

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