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Item (Deep Learning: Predict El Niño–southern oscillation)

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ID 4e904b8532d94f25a06b8f85537e16c5
Item forecasting_enso_via_correlation_time_series_deep_learning.ipynb
Item Type file
Owner esri_notebook
Uploaded Apr 25, 2019
Modified Apr 25, 2019
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Name N/A
Title Deep Learning: Predict El Niño–southern oscillation
Type Notebook
Type Keywords Notebook, Python
Description ENSO can have tremendous potential impact such as droughts, floods, and tropical storms. Accurate characterization of ENSO is critical for understanding the trends. In climate science, ENSO is characterized through Southern Oscillation Index (SOI), a standardized index based on the observed sea level pressure differences between Tahiti and Darwin, Australia. Predicting SOI is the first step of ENSO forecasting which this notebook attempts to do using time series analysis using a LSTM model.
Tags LSTM, ENSO, weather, climate, prediction, deep learning, keras, time series, El Niño, SOI
Snippet This example uses correlation analysis and time series analysis to predict El Niño–Southern Oscillation (ENSO) based on climate variables and indices.
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License Information N/A
Culture english (united states)
Properties {"notebookRuntimeName":"ArcGIS Notebook Python 3 Standard","notebookRuntimeVersion":"10.7.1"}
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Last Modified N/A
Size 1947910
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Comments Enabled true
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# Views 118
CategoriesData Science and Analysis
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