Update README.md

README.md

@@ -70,28 +70,28 @@ pred = testModel(model,
 
 Several examples related to the above papers are presented here. **Click the title link** to see each example.
 
-## [1.Train a LSTM model to learn SMAP soil moisture](example/demo-LSTM-Tutorial.ipynb)
-The example dataset is embedded in this repo and can be found [here](example/data).
-You can also use [this script](example/train-lstm.py) to train model if you don't want to work with Jupyter Notebook.
+## [1.Train a LSTM data integration model to make streamflow forecast](example/StreamflowExample-DI.py)
+The dataset used is NCAR CAMELS dataset. Download CAMELS following [this link](https://ral.ucar.edu/solutions/products/camels). 
+Please download both forcing, observation data `CAMELS time series meteorology, observed flow, meta data (.zip)` and basin attributes `CAMELS Attributes (.zip)`. 
+Put two unzipped folders under the same directory, like `your/path/to/Camels/basin_timeseries_v1p2_metForcing_obsFlow`, and `your/path/to/Camels/camels_attributes_v2.0`. Set the directory path `your/path/to/Camels`
+as the variable `rootDatabase` inside the code later.
+
+Computational benchmark: training of CAMELS data (w/ or w/o data integration) with 671 basins, 10 years, 300 epochs, in ~1 hour with GPU.
 
 Related papers:  
-Fang et al. (2017), [Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL075619), Geophysical Research Letters.
+Feng et al. (2020). [Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales](https://doi.org/10.1029/2019WR026793). Water Resources Research.
 
 ## [2.Training a Transformer model](https://zenodo.org/records/13664154)
 
 Related papers:  
 Liu, J., Bian, Y., Lawson, K., & Shen, C. (2024). Probing the limit of hydrologic predictability with the Transformer network. Journal of Hydrology, 637, 131389.
 
-## [3.Train a LSTM data integration model to make streamflow forecast](example/StreamflowExample-DI.py)
-The dataset used is NCAR CAMELS dataset. Download CAMELS following [this link](https://ral.ucar.edu/solutions/products/camels). 
-Please download both forcing, observation data `CAMELS time series meteorology, observed flow, meta data (.zip)` and basin attributes `CAMELS Attributes (.zip)`. 
-Put two unzipped folders under the same directory, like `your/path/to/Camels/basin_timeseries_v1p2_metForcing_obsFlow`, and `your/path/to/Camels/camels_attributes_v2.0`. Set the directory path `your/path/to/Camels`
-as the variable `rootDatabase` inside the code later.
-
-Computational benchmark: training of CAMELS data (w/ or w/o data integration) with 671 basins, 10 years, 300 epochs, in ~1 hour with GPU.
+## [3.Train a LSTM model to learn SMAP soil moisture](example/demo-LSTM-Tutorial.ipynb)
+The example dataset is embedded in this repo and can be found [here](example/data).
+You can also use [this script](example/train-lstm.py) to train model if you don't want to work with Jupyter Notebook.
 
 Related papers:  
-Feng et al. (2020). [Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales](https://doi.org/10.1029/2019WR026793). Water Resources Research.
+Fang et al. (2017), [Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL075619), Geophysical Research Letters.
 
 ## [4.Train LSTM and CNN-LSTM models for prediction in ungauged regions](example/PUR/trainPUR-Reg.py)
 The dataset used is also NCAR CAMELS. Follow the instructions in the first example above to download and unzip the dataset. Use [this code](example/PUR/testPUR-Reg.py) to test your saved models after training finished.