Detection Of Multi-layer INterconnected Occurrences for Spatial Extreme Events
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Updated
Jun 5, 2026 - Jupyter Notebook
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compound-extremes
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MST.PMDN: 'torch for R' package implementing the deep Multivariate Skew t-Parsimonious Mixture Density Network
r deep-neural-networks deep-learning torch multivariate-distributions environmental-data-science compound-events compound-extremes
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Aug 11, 2026 - R
Reproducible pipeline for the integrated downscaling–BMA–BiLSTM framework: CMIP6 GCM evaluation, two-track downscaling (LARS-WG 8 + DQM), EM-based Bayesian Model Averaging, deep-ensemble streamflow projection, and drought / ETCCDI / compound-extreme analysis. Demonstrated on the Haraz basin, northern Iran.
deep-learning reproducible-research iran hydrology drought uncertainty-quantification cmip6 climate-change bilstm bayesian-model-averaging statistical-downscaling quantile-mapping deep-ensemble compound-extremes streamflow-prediction lars-wg
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Aug 12, 2026 - Python
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