Techniques used to predict climate risks: a brief literature survey

Ruchika Nanwani (), Md Mahmudul Hasan () and Silvia Cirstea ()
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Ruchika Nanwani: Anglia Ruskin University
Md Mahmudul Hasan: Anglia Ruskin University
Silvia Cirstea: Anglia Ruskin University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 118, issue 2, No 3, 925-951

Abstract: Abstract 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.

Keywords: Weather prediction; Recurrent neural networks; Long short-term memory Networks; Weather forecasting; Multilayer perceptron; Machine learning; Deep learning; Convolution neural networks; Gated recurrent unit; Relative position-based self-attention mechanism; Bayesian neural networks; Auto-regressive integrated moving average (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06046-2

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