PROJECTS254/BAYESIAN_OPTIMIZED_GRU_XGBOOST_FOR_FLOOD_PREDICTION

📝 Hugging Face Model Card: Hybrid GRU-XGBoost Flood Predictor

language: - en tags: - flood-prediction - time-series - hybrid-model - gru - xgboost - onnx - hydrology metrics: - r2 - rmse - mae - nse pipeline_tag: tabular-regression

🌊 Hybrid GRU-XGBoost Flood Predictor

This repository hosts a high-precision, production-grade hybrid Machine Learning pipeline designed to predict river water levels and issue automated flood warnings. By combining sequential feature extraction with non-linear gradient boosting, the model achieves exceptional generalization and robustness.

🛠️ Model Architecture

The pipeline utilizes a sequential hybrid layout:

1. Deep Sequential Feature Extraction (GRU): A gated recurrent unit network processes scaled hydrological time-series inputs to extract temporal dynamics and hidden states.
2. Non-Linear Residual Regressor (XGBoost): The raw features are combined with the GRU's hidden states to form a fused feature matrix. A highly tuned XGBoost regressor processes this matrix to produce the final water level prediction.
3. ONNX Optimization: The deep GRU feature extractor is compiled to the ONNX format, eliminating heavy PyTorch/TensorFlow dependencies and enabling instant CPU inference.

📊 Features & Preprocessing

Using Recursive Feature Elimination with Cross-Validation (RFECV), the optimal feature set was minimized to two critical real-time features:

• fused_rainfall_mm: Spatially and temporally aggregated rainfall observations.
• river_flow_rate_m3_s: Upstream volumetric water flow rate.

Signal Processing & Preprocessing

• Variational Mode Decomposition (VMD): Used during signal analysis to denoise inputs and target records.
• Standardization: Features and target variables are scaled independently using a StandardScaler fitted exclusively on historical training data to prevent temporal data leakage.

📈 Performance Benchmarks

Both models were validated on an independent unseen test set (15% split), evaluated across standard regression metrics and binary classification equivalents (Flood vs No-Flood based on the 80th percentile threshold):

Model	Train R²	Test R²	RMSE	MAE	NSE (Nash-Sutcliffe)	Flood Detection F1-Score
Standalone GRU	0.987	0.986	0.083m	0.067m	0.986	0.846
Standalone XGBoost	0.998	0.987	0.081m	0.064m	0.987	0.880
Hybrid GRU-XGBoost	0.999	0.987	0.079m	0.064m	0.987	0.800

Statistical Validation

• Residual Diagnostics: Residuals show zero autocorrelation with a Durbin-Watson score of 2.04, confirming that sequential dependencies were fully resolved.
• Diebold-Mariano Test: Confirms statistically reliable prediction capability compared to standalone architectures.

🚀 How to Run Inference

You can load the pipeline and perform inference in Python using onnxruntime, xgboost, and joblib:

import joblib
import json
import numpy as np
import onnxruntime as ort
import xgboost as xgb

# 1. Load artifacts
scaler_X = joblib.load('scaler_X.pkl')
scaler_y = joblib.load('scaler_y.pkl')
ort_session = ort.InferenceSession('gru_model.onnx')

hybrid_xgb = xgb.XGBRegressor()
hybrid_xgb.load_model('xgb_flood_model.json')

# 2. Input values (e.g., rainfall, flow rate)
raw_inputs = np.array([[12.5, 145.2]]) # Shape: (1, 2)

# 3. Transform inputs
scaled_inputs = scaler_X.transform(raw_inputs)
gru_inputs = scaled_inputs.reshape(1, 1, 2).astype(np.float32)

# 4. Extract GRU temporal embeddings
onnx_inputs = {ort_session.get_inputs()[0].name: gru_inputs}
hidden_states = ort_session.run(None, onnx_inputs)[0]

# 5. Combine and Predict
fused_features = np.hstack((scaled_inputs, hidden_states))
scaled_prediction = hybrid_xgb.predict(fused_features)

# 6. Inverse Transform output
water_level = scaler_y.inverse_transform(scaled_prediction.reshape(-1, 1)).item()
print(f"Predicted River Level: {water_level:.2f} meters")