kozy9/GWLSTM

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

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')