Prediction of weather forecasting using artificial neural networks
In one sentence. Applies artificial neural networks to predict daily weather for several cities, claiming very low deviations, though without a quantified benchmark in the abstract.
Abstract
Currently, weather forecasting is the most commonly discussed topic by social and economic activists. It is also attracting widespread interest due to its application in various public and private sectors that include marine, agriculture, air traffic, and forestry. Recent developments have made climatic changes happen at a dramatic rate, making old methods of weather forecasting less effective, more hectic, and unreliable. Improved and efficient methods of weather prediction are needed to overcome these difficulties. This paper describes machine learning approaches using artificial neural networks to predict the weather of a particular city and compare the different weather conditions in different cities. We demonstrate empirically that Artificial Neural Networks produce very low deviations hence providing nearly accurate results for weather forecasts on a daily basis.
Labels
- Topic
- Local & station forecasting
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.22201/icat.24486736e.2023.21.2.1698 OpenAlex:W4381707588
- Added
- 2026-10-22
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