Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks
| Autor: | Bhavya Alankar, Shafqat Ul Ahsaan, Nowsheena Yousf |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Atmosphere (unit)
Artificial neural network Computer science business.industry Weather forecasting Training (meteorology) Predictive analytics computer.software_genre Machine learning Backpropagation Task (project management) Back propagation neural network ComputerApplications_GENERAL Artificial intelligence business computer Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9789811393297 |
| DOI: | 10.1007/978-981-13-9330-3_10 |
| Popis: | Weather can be elucidated as the appearance of atmosphere for a short span of time for a specific place. Weather an important factor in our day-to-day lives and also the one that controls how and what people should do. For researchers and scientists, weather has been the most demanding task. The practice of technology that has been practiced to presume the conditions of atmosphere for a given area at a specific time is weather forecasting. Rationally accurate forecasts are obtained because of modern weather forecasting methods that involve computer models, observations and information about current patterns, and trends. Due to the presence of nonlinearity in climatic data, neural networks have been the satisfactory for prediction of weather. An algorithm based on artificial neural networks is used for predicting the future weather as the artificial neural network (ANN) package supports a number of learning or training algorithms. The benefits of using artificial neural networks for predicting the weather have been presented in this paper. In this paper, weather predictions are made by building training and testing datasets with the usage of learning algorithms back propagation and resilient back propagation. |
| Databáze: | OpenAIRE |
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