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.

• 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.

python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -r requirements.txt

MIT License (see LICENSE).

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

For any inquiries, please contact:

• std_farzad.asgari@khu.ac.ir
• khufarzadasgari@gmail.com