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Public Datasets and Codes for Data-driven discovery of soil moisture flow governing equation: A sparse regression framework. Water Resources Research.

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Discovering Soil Moisture Flow Equation from Data via Sparse Regression

Public Datasets and Codes for Data-driven discovery of soil moisture flow governing equation: A sparse regression framework. Water Resources Research.

Requirements

  • Python 3.6.13
  • Matplotlib
  • Numpy
  • Pandas
  • Tensorflow 1.14

Datasets

See Data folder, details can be found in journal article. This dataset is generated by solving Richardson-Richards equation by Ross method. Please see:

Zha, Y., Shi, L., Ye, M., & Yang, J. (2013). A generalized Ross method for two-and three-dimensional variably saturated flow. Advances in Water Resources, 54, 67-77. https://doi.org/10.1016/j.advwatres.2013.01.002

Codes

Use step1.py (finite methods for clean data and automatic differentiation methods for noisy data) to generate a candidate library, then input into step2 sparse regression.

Citations

Song, W., Shi, L., Wang, L., Wang, Y., & Hu, X. (2022). Data-driven discovery of soil moisture flow governing equation: A sparse regression framework. Water Resources Research, 58, e2022WR031926. https://doi.org/10.1029/2022WR031926

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Public Datasets and Codes for Data-driven discovery of soil moisture flow governing equation: A sparse regression framework. Water Resources Research.

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