FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale

1. 1
   Gather Geospatial Data

   Collect comprehensive geospatial data layers relevant to flood and landslide hazards, including Digital Elevation Models (DEMs), land cover, soil types, rainfall data, and historical event locations, for your target region.
2. 2
   Preprocess and Align Data

   Clean, normalize, and spatially align all collected data layers to a consistent grid resolution and projection. Ensure all features are ready for deep learning model input.
3. 3
   Design Spatially-Adaptive DL Architecture

   Develop a deep learning model architecture (e.g., using CNNs or Transformers) that incorporates mechanisms for spatially-adaptive feature fusion. This could involve attention layers or spatially-varying convolutions to capture local interdependencies.
4. 4
   Implement Ensemble Learning Strategy

   Integrate multiple deep learning models or varying configurations of your chosen architecture into an ensemble framework. Experiment with strategies like bagging, boosting, stacking, or simple averaging of predictions to enhance robustness.
5. 5
   Train and Validate Model

   Train the ensemble deep learning model using historical multi-hazard event data. Validate its performance using appropriate geospatial and classification metrics (e.g., AUC, F1-score, precision, recall) on an independent test set.
6. 6
   Generate Susceptibility Maps

   Apply the trained ensemble model to predict multi-hazard susceptibility across the entire target region. Visualize the output as high-resolution susceptibility maps for floods and landslides, identifying critical areas.

import numpy as np

# Simulate predictions from multiple deep learning models (e.g., for flood and landslide scores)
# Each row represents a spatial location, each column a hazard susceptibility score
model1_flood_landslide_preds = np.random.rand(100, 2)
model2_flood_landslide_preds = np.random.rand(100, 2)
model3_flood_landslide_preds = np.random.rand(100, 2)

# Implement a simple ensemble averaging for multi-hazard susceptibility
# This combines the prediction for each hazard at each location
ensemble_multi_hazard_preds = (model1_flood_landslide_preds + model2_flood_landslide_preds + model3_flood_landslide_preds) / 3

print("Shape of ensemble predictions (locations, hazards):")
print(ensemble_multi_hazard_preds.shape)
print("\nExample ensemble susceptibility for first 3 locations (Flood, Landslide):")
print(ensemble_multi_hazard_preds[:3])

# In a full FL-MHSM, these would be actual DL model outputs over a spatial grid,
# and the ensemble strategy could be more sophisticated (e.g., weighted average, stacking).

• Paperarxiv.org