https://github.com/farzadasgari/evap

Lake surface water evaporation modeling using remote-sensed water quality parameters (CHL, CDOM, TSM, temperature) and Bayesian-optimized LSTM/GRU hybrids validated against Penman-FAO.
https://github.com/farzadasgari/evap

bayesian-optimization cdec era5 evaporation gated-recurrent-units google-earth-engine gru hybrid-model long-short-term-memory lstm python recurrent-neural-networks remote-sensing satellite satellite-imagery sentinel-2 snap water water-engineering water-quality

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Lake surface water evaporation modeling using remote-sensed water quality parameters (CHL, CDOM, TSM, temperature) and Bayesian-optimized LSTM/GRU hybrids validated against Penman-FAO.

• Host: GitHub
• URL: https://github.com/farzadasgari/evap
• Owner: farzadasgari
• License: mit
• Created: 2025-09-08T20:39:36.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-09-29T17:53:20.000Z (9 months ago)
• Last Synced: 2025-09-29T19:34:04.707Z (9 months ago)
• Topics: bayesian-optimization, cdec, era5, evaporation, gated-recurrent-units, google-earth-engine, gru, hybrid-model, long-short-term-memory, lstm, python, recurrent-neural-networks, remote-sensing, satellite, satellite-imagery, sentinel-2, snap, water, water-engineering, water-quality
• Language: Jupyter Notebook
• Homepage:
• Size: 75.1 MB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


   
Lake Surface Water Evaporation Modeling with Remote Sensing and Hybrid Deep Learning



This repository supports a research study on estimating lake surface water evaporation using satellite-derived water quality (WQ) parameters and in-situ meteorological (MG) data. We develop Bayesian Optimization (BO)-tuned deep learning architectures (BO-LSTM, BO-GRU) and compare them with their non-optimized counterparts (LSTM, GRU) and a physically based Penman-FAO formulation (baseline).

> Status: Active research codebase (manuscript in preparation). Structure and APIs may change.

---

## Key Contributions

- Focus on open surface water evaporation.

- Integrates remote sensing–derived WQ parameters (CHL, CDOM, TSM, temperature) with MG drivers.

- Hybrid deep learning: BO-LSTM and BO-GRU (Bayesian hyperparameter optimization).

- Benchmark against Penman-FAO physical model.

- Feature attribution using SHAP for interpretability.

- Demonstrates viability of WQ-only predictors where MG data are sparse.

---

## Installation

```bash

python -m venv .venv

source .venv/bin/activate  # or .venv\Scripts\activate on Windows

pip install -r requirements.txt

```

---

## License

MIT License (see [LICENSE](https://github.com/farzadasgari/evap?tab=MIT-1-ov-file)).

---

## Disclaimer

This repository is a research vehicle. Model outputs should not be treated as operational hydrological guidance without independent verification.

---

## Contact

For any inquiries, please contact:

- std_farzad.asgari@khu.ac.ir

- khufarzadasgari@gmail.com

---

## Links

### Farzad Asgari

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### Seyed Hossein Mohajeri

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