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license: mit
tags:
- time-series
- forecasting
- lstm
- hydrology
- groundwater
---
# LSTM Groundwater Level Forecasting — UK
A tuned LSTM model for single-step monthly groundwater level forecasting
using meteorological variables as exogenous inputs.
## Model Details
| Parameter | Value |
|---|---|
| Architecture | LSTM(128) → Dropout(0.1) → Dense(32) → Dense(1) |
| Framework | TensorFlow / Keras |
| Task | Single-step monthly forecasting |
| Lookback window | 24 months |
| Input features | water_level, temperature, precipitation, wind_speed |
| Tuning method | Bayesian Optimisation (Keras Tuner, 20 trials) |
## Data Splits
| Split | Period | Months |
|---|---|---|
| Training | 1944-01-01 → 2007-10-01 | 766 |
| Validation | 2007-11-01 → 2015-10-01 | 96 |
| Test | 2015-11-01 → 2023-10-01 | 96 |
## Best Hyperparameters
| Parameter | Value |
|---|---|
| LSTM layers | 2 |
| Units | 64 |
| Dropout | 0.1 |
| Learning rate | 0.000773 |
| Batch size | 32 |
## Test Set Performance
| Metric | Value |
|---|---|
| RMSE | 2.9386 m |
| MAE | 2.397 m |
| MAPE | 3.671% |
| R² | 0.5505 |
| NSE | 0.5505 |
> This model is part of a benchmark study comparing SARIMAX, LSTM, and TCN
> for UK groundwater level forecasting.
## Important Note
Contemporaneous meteorological variables are used as inputs at forecast time
(oracle assumption). Future met values are treated as known — consistent with
the experimental setup used across all models in this study.
## Repository Contents
```
├── lstm_model.keras # Trained Keras model
├── scaler_X.pkl # Feature scaler (MinMaxScaler)
├── scaler_y.pkl # Target scaler (MinMaxScaler)
├── model_config.json # Config, hyperparameters & metrics
├── inference.py # Load model & generate forecasts
└── README.md # This file
```
## Quick Start
```python
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
import joblib, pandas as pd, numpy as np
model = load_model(hf_hub_download('kozy9/GWLSTM', 'lstm_model.keras'))
scaler_X = joblib.load(hf_hub_download('kozy9/GWLSTM', 'scaler_X.pkl'))
scaler_y = joblib.load(hf_hub_download('kozy9/GWLSTM', 'scaler_y.pkl'))
# Provide a 24-month window of features
X_window = pd.DataFrame({
'water_level' : [...], # 24 values
'temperature' : [...],
'precipitation': [...],
'wind_speed' : [...],
})
X_scaled = scaler_X.transform(X_window)
X_input = X_scaled.reshape(1, 24, 4)
y_scaled = model.predict(X_input)
pred = scaler_y.inverse_transform(y_scaled)[0][0]
print(f'Next month forecast: {pred:.2f} m')
```
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