nwm-post-processing-with-lstm's Introduction

This is code to post process the National Water Model output with Long Short Term Memory networks.

The LSTM codebase is copied from `https://github.com/kratzert/ealstm_regional_modeling` (Old codebase)

Also running the LSTM with the new codebase here: `https://github.com/kratzert/lstm_based_hydrology.git` (New codebase)

Content of main repository

`main*.py` Main python file used for training and evaluating of our models, as well as to perform the robustness analysis using the `ealstm_regional_modeling` codebase.

data/ contains the list of basins (USGS gauge ids) considered in our study

`jupyter_notes/` working notebooks calculating and plotting the results of our study. These notebooks will be updated and when in their final versions will be your starting point for duplicating results.

`environment_gpu.yml` can be used with Anaconda or Miniconda to create an environment with all packages needed.

configs/ Configuration files for running the LSTM with the newer codebase `(lstm_based_hydrology)`

`ig_nwm_lstm.py` This script is used to calculate the integrated gradients of the LSTM runs from the old codebase. The 'baselines' are all set to zero.

`ig_nwm_lstm_precip.py` This is the same as above, but this particular calculation sets the baseline of the precipitation to -mean/stdev, leaving the other baselines at zero.

These experiments are trained on water years 2004 - 2013 and tested on water years 1994 - 2002.

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pjbutcher / nwm-post-processing-with-lstm Goto Github PK

nwm-post-processing-with-lstm's Introduction

This is code to post process the National Water Model output with Long Short Term Memory networks.

The LSTM codebase is copied from `https://github.com/kratzert/ealstm_regional_modeling` (Old codebase)

Also running the LSTM with the new codebase here: `https://github.com/kratzert/lstm_based_hydrology.git` (New codebase)

Content of main repository

`main*.py` Main python file used for training and evaluating of our models, as well as to perform the robustness analysis using the `ealstm_regional_modeling` codebase.

data/ contains the list of basins (USGS gauge ids) considered in our study

`jupyter_notes/` working notebooks calculating and plotting the results of our study. These notebooks will be updated and when in their final versions will be your starting point for duplicating results.

`environment_gpu.yml` can be used with Anaconda or Miniconda to create an environment with all packages needed.

configs/ Configuration files for running the LSTM with the newer codebase `(lstm_based_hydrology)`

`ig_nwm_lstm.py` This script is used to calculate the integrated gradients of the LSTM runs from the old codebase. The 'baselines' are all set to zero.

`ig_nwm_lstm_precip.py` This is the same as above, but this particular calculation sets the baseline of the precipitation to -mean/stdev, leaving the other baselines at zero.

These experiments are trained on water years 2004 - 2013 and tested on water years 1994 - 2002.

nwm-post-processing-with-lstm's People

Contributors

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

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Microsoft

Google

Alibaba

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Tencent

Jobs

pjbutcher / nwm-post-processing-with-lstm Goto Github PK

nwm-post-processing-with-lstm's Introduction

This is code to post process the National Water Model output with Long Short Term Memory networks.

The LSTM codebase is copied from https://github.com/kratzert/ealstm_regional_modeling (Old codebase)

Also running the LSTM with the new codebase here: https://github.com/kratzert/lstm_based_hydrology.git (New codebase)

Content of main repository

main*.py Main python file used for training and evaluating of our models, as well as to perform the robustness analysis using the ealstm_regional_modeling codebase.

data/ contains the list of basins (USGS gauge ids) considered in our study

jupyter_notes/ working notebooks calculating and plotting the results of our study. These notebooks will be updated and when in their final versions will be your starting point for duplicating results.

environment_gpu.yml can be used with Anaconda or Miniconda to create an environment with all packages needed.

configs/ Configuration files for running the LSTM with the newer codebase (lstm_based_hydrology)

ig_nwm_lstm.py This script is used to calculate the integrated gradients of the LSTM runs from the old codebase. The 'baselines' are all set to zero.

ig_nwm_lstm_precip.py This is the same as above, but this particular calculation sets the baseline of the precipitation to -mean/stdev, leaving the other baselines at zero.

These experiments are trained on water years 2004 - 2013 and tested on water years 1994 - 2002.

nwm-post-processing-with-lstm's People

Contributors

Recommend Projects

Recommend Topics

Recommend Org

Jobs

The LSTM codebase is copied from `https://github.com/kratzert/ealstm_regional_modeling` (Old codebase)

Also running the LSTM with the new codebase here: `https://github.com/kratzert/lstm_based_hydrology.git` (New codebase)

`main*.py` Main python file used for training and evaluating of our models, as well as to perform the robustness analysis using the `ealstm_regional_modeling` codebase.

`jupyter_notes/` working notebooks calculating and plotting the results of our study. These notebooks will be updated and when in their final versions will be your starting point for duplicating results.

`environment_gpu.yml` can be used with Anaconda or Miniconda to create an environment with all packages needed.

configs/ Configuration files for running the LSTM with the newer codebase `(lstm_based_hydrology)`

`ig_nwm_lstm.py` This script is used to calculate the integrated gradients of the LSTM runs from the old codebase. The 'baselines' are all set to zero.

`ig_nwm_lstm_precip.py` This is the same as above, but this particular calculation sets the baseline of the precipitation to -mean/stdev, leaving the other baselines at zero.