Deterministic weather forecasting models based on intelligent predictors: A survey
Weather forecasting is the practice of predicting the state of the atmosphere for a given location based on different weather parameters. Weather forecasts are made by gathering data about the current state of the atmosphere. Accurate weather forecasting has proven to be a challenging task for meteo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820304729 |
| _version_ | 1811334671357706240 |
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| author | K.U. Jaseena Binsu C. Kovoor |
| author_facet | K.U. Jaseena Binsu C. Kovoor |
| author_sort | K.U. Jaseena |
| collection | DOAJ |
| description | Weather forecasting is the practice of predicting the state of the atmosphere for a given location based on different weather parameters. Weather forecasts are made by gathering data about the current state of the atmosphere. Accurate weather forecasting has proven to be a challenging task for meteorologists and researchers. Weather information is essential in every facet of life like agriculture, tourism, airport system, mining industry, and power generation. Weather forecasting has now entered the era of Big Data due to the advancement of climate observing systems like satellite meteorological observation and also because of the fast boom in the volume of weather data. So, the traditional computational intelligence models are not adequate to predict the weather accurately. Hence, deep learning-based techniques are employed to process massive datasets that can learn and make predictions more effectively based on past data. The effective implementation of deep learning in various domains has motivated its use in weather forecasting and is a significant development for the weather industry. This paper provides a thorough review of different weather forecasting approaches, along with some publicly available datasets. This paper delivers a precise classification of weather forecasting models and discusses potential future research directions in this area. |
| first_indexed | 2024-04-13T17:12:33Z |
| format | Article |
| id | doaj.art-18c1dd05569d4629aea5a4493218b797 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T17:12:33Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-18c1dd05569d4629aea5a4493218b7972022-12-22T02:38:13ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134633933412Deterministic weather forecasting models based on intelligent predictors: A surveyK.U. Jaseena0Binsu C. Kovoor1Division of Information Technology, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, IndiaCorresponding author.; Division of Information Technology, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, IndiaWeather forecasting is the practice of predicting the state of the atmosphere for a given location based on different weather parameters. Weather forecasts are made by gathering data about the current state of the atmosphere. Accurate weather forecasting has proven to be a challenging task for meteorologists and researchers. Weather information is essential in every facet of life like agriculture, tourism, airport system, mining industry, and power generation. Weather forecasting has now entered the era of Big Data due to the advancement of climate observing systems like satellite meteorological observation and also because of the fast boom in the volume of weather data. So, the traditional computational intelligence models are not adequate to predict the weather accurately. Hence, deep learning-based techniques are employed to process massive datasets that can learn and make predictions more effectively based on past data. The effective implementation of deep learning in various domains has motivated its use in weather forecasting and is a significant development for the weather industry. This paper provides a thorough review of different weather forecasting approaches, along with some publicly available datasets. This paper delivers a precise classification of weather forecasting models and discusses potential future research directions in this area.http://www.sciencedirect.com/science/article/pii/S1319157820304729Weather forecastingArtificial neural networksDeep learningAutoencodersRecurrent neural networks |
| spellingShingle | K.U. Jaseena Binsu C. Kovoor Deterministic weather forecasting models based on intelligent predictors: A survey Journal of King Saud University: Computer and Information Sciences Weather forecasting Artificial neural networks Deep learning Autoencoders Recurrent neural networks |
| title | Deterministic weather forecasting models based on intelligent predictors: A survey |
| title_full | Deterministic weather forecasting models based on intelligent predictors: A survey |
| title_fullStr | Deterministic weather forecasting models based on intelligent predictors: A survey |
| title_full_unstemmed | Deterministic weather forecasting models based on intelligent predictors: A survey |
| title_short | Deterministic weather forecasting models based on intelligent predictors: A survey |
| title_sort | deterministic weather forecasting models based on intelligent predictors a survey |
| topic | Weather forecasting Artificial neural networks Deep learning Autoencoders Recurrent neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820304729 |
| work_keys_str_mv | AT kujaseena deterministicweatherforecastingmodelsbasedonintelligentpredictorsasurvey AT binsuckovoor deterministicweatherforecastingmodelsbasedonintelligentpredictorsasurvey |