HydroSense-Kenya is a Python-based scientific computing project that models soil-water balance, estimates water deficits, simulates future soil moisture under climate uncertainty, and recommends optimized irrigation schedules for efficient water management in agriculture.
# HydroSense-Kenya
## Overview
HydroSense-Kenya is a scientific computing project that models water management and irrigation decision support for agricultural zones in Kenya. The project combines weather observations, soil sensor measurements, and crop-specific parameters to estimate evapotranspiration, monitor soil moisture, and support efficient water use.
## Project Objectives
* Analyse agricultural and environmental datasets.
* Model soil water balance using scientific computing techniques.
* Estimate evapotranspiration using weather variables.
* Visualise rainfall and soil moisture trends.
* Develop numerical methods and simulations for irrigation planning.
* Demonstrate reproducible scientific computing workflows.
## Project Structure
```text
HydroSense-Kenya/
│
├── data/
│ └── raw/
│ ├── weather_daily.csv
│ ├── soil_sensor_data.csv
│ └── crop_zone_parameters.csv
│
├── notebooks/
│ └── Level_1_Problem_Framing.ipynb
│
├── reports/
│ ├── data_dictionary.md
│ ├── level1_assumptions.md
│ └── level1_problem_statement.md
│
├── src/
│ └── simulation.py
│
├── tests/
│
└── README.md
```
## Technologies Used
* Python
* NumPy
* Pandas
* Matplotlib
* Jupyter Notebook
## Current Progress
* Level 1: Problem Framing and Water Balance Model ✅
* Level 2: Vectorization and Numerical Error Analysis ⏳
* Level 3: Numerical Methods ⏳
* Level 4: Data Analysis and Visualization ⏳
* Level 5: Simulation and Optimization ⏳
* Level 6: Testing and Reproducibility ⏳
HydroSense-Kenya is a scientific computing project that applies numerical modeling, data analysis, optimization, and validation techniques to support irrigation and water management decisions using environmental and agricultural datasets.
## Author
Scientific Computing Project – HydroSense-Kenya