rename to HydroForecast and update env for torchhydro and hydrodata

README.md

@@ -1,21 +1,21 @@
 <!--
  * @Author: Wenyu Ouyang
  * @Date: 2023-10-29 17:35:04
- * @LastEditTime: 2023-11-30 14:33:58
+ * @LastEditTime: 2024-02-12 15:49:47
  * @LastEditors: Wenyu Ouyang
- * @Description: USE AI TO FORECAST FLOOD
- * @FilePath: \AIFloodForecast\README.md
+ * @Description: Hydro forecast
+ * @FilePath: \HydroForecast\README.md
  * Copyright (c) 2023-2024 Wenyu Ouyang. All rights reserved.
 -->
-# AIFloodForecast
+# HydroForecast
 
-It's a project for flood forecasting based on public data and artificial intelligence technology (especially deep learning). The project is still in progress, and the current version is only a prototype.
+It's a project for hydrological forecasting based on big data and artificial intelligence technology (especially deep learning). The project is still in progress, and the current version is only a prototype.
 
 ## Introduction
 
 The project is based on the [PyTorch](https://pytorch.org/) framework, and the main code is written in Python. 
 
-It is divided into two parts: data processing and model training. The data processing part is currently mainly based on our hydro-opendata project, which is used to download, process, read and write public data source related to flood forecasting. The model training part is mainly based on the [torchhydro](https://www.pytorchlightning.ai/) framework, which is our self-developed framework focusing on hydrological forecasting.
+It is divided into two parts: data processing and model training. The data processing part is currently mainly based on our [hydrodata](https://github.com/iHeadWater/hydrodata) project, which is used to download, process, read and write public data source related to flood forecasting. The model training part is mainly based on the [torchhydro](https://github.com/iHeadWater/torchhydro) and [hydromodel](https://github.com/iHeadWater/hydromodel) framework, which is our self-developed framework focusing on hydrological forecasting
 
 The idea of the project is to use the public data source from data-rich regions such as United States and Europe to train a foundation model. Then we use the trained model to predict river stage or discharge in data-poor regions such as China (actually ther are much data in China, but most are not accessible to the public). The current version is mainly based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model with precipitation from [Global Precipitation Measurement (GPM)](https://gpm.nasa.gov/) and Global Forecast System (GFS) as input and river stage or discharge as output.
 
@@ -27,7 +27,7 @@ The project is based on Python 3.10. The required packages are listed in `env.ym
 # simply install a new environment AIFF
 conda env create -f env.yml
 # then we install packages developed by ourselves as follows
-conda activate AIFF
+conda activate HydroForecast
 # xxx means your Github username; xxxxx means the name of the package; xx means the git-branch of the package
 pip install git+ssh://git@github.com/xxx/xxxxx.git@xx
 ```
@@ -36,10 +36,7 @@ The packages we developed are listed as follows in [iHeadWater](https://github.c
 
 ```bash
 torchhydro
-hydro_opendata
-hydro_privatedata
-hydroutils
-hydrodataset
+hydromodel
 ```
 
 We'd better use the latest version of the packages. You can check the version of the packages in Github.

env.yml

@@ -1,4 +1,4 @@
-name: AIFF
+name: HydroForecast
 channels:
   - pytorch
   - conda-forge
@@ -7,3 +7,7 @@ dependencies:
   - python=3.10
   - numpy=1.23
   - pytorch=1.12
+  - pip
+  - pip:
+    - hydromodel
+    - torchhydro