A tool to assess the static depth to water (DTW) in crystalline basement aquifers of Africa
# hydrodepthml
## Table of Contents
- 1. Project Overview
- 2. Installation Instructions
- Prerequisites
- Step 1: Clone the Repository
- Step 2: Create Environment
- Step 3: Install the Package
- 3. Storage Requirements
- 4. Grid-Based Processing Strategy
- Coordinate Reference System
- Grid-Based Approach
- 5. How to Use
- 6. Acknowledgements
## 1. Project Overview
This project aims to leverage geospatial (GIS) and climatic data to develop a tool based on machine learning modeling to assess the static depth to water (DTW) in crystalline basement aquifers of Africa. Accurate water table depth predictions are crucial for sustainable water resource management, especially in regions dependent on rainfed agriculture.
The model is trained using one-time measurements of water table depth in the following countries: Benin, Burkina Faso, Guinea, Mali, Niger, Togo, TChad
The training data includes:
- Climatic variables (daily precipitation and NDVI).
- Topographic and hydrological features derived from Digital Elevation Models (DEMs) and GIS analysis.
## 2. Installation Instructions
These instructions use graphical interfaces (Anaconda Navigator and GitHub Desktop) for ease of use.
> [!NOTE]
> If you prefer using command-line tools, you can use `git clone`, `conda env create -f environment.yml`, and standard conda/pip commands. The repository includes an `environment.yml` file with all dependencies.
### Prerequisites
1. **Anaconda** (full distribution, includes Navigator)
- Download:
anaconda.com
- Install with default options
- **Important:** Use a recent version (2023 or newer recommended)
2. **GitHub Desktop**
- Download:
desktop.github.com
- Install and sign in with your GitHub account (create one if needed)
3. **NASA EarthData Account** (free, required for downloading satellite data)
- Sign up:
urs.earthdata.nasa.gov
### Step 1: Clone the Repository with GitHub Desktop
1. Open **GitHub Desktop**
2. Click **File** → …