Self-Supervised Temporal Pattern Mining for wildfire evacuation logistics networks under real-time policy constraints
Source: Dev.to
Introduction: The Learning Journey That Sparked This Research
It was during the 2023 wildfire season, while I was analyzing evacuation‑route failures in Northern California, that I had my breakthrough realization. I had been experimenting with traditional supervised‑learning models for predicting evacuation bottlenecks, but they kept failing when policy constraints changed mid‑evacuation. The models were trained on historical data, yet real‑time policy shifts—such as sudden road closures or shelter‑capacity changes—rendered them practically useless.
While exploring self‑supervised learning papers from the computer‑vision domain, I discovered something fascinating: the same techniques that allow models to learn representations from unlabeled images could be adapted to temporal sequences in evacuation logistics. My research into contrastive‑learning approaches revealed that, by treating different time windows of evacuation data as distinct “views” of the same underlying process, I could build models that learned robust temporal patterns without explicit labels. This was the genesis of my work on self‑supervised temporal pattern mining for wildfire evacuation networks.
Wildfire evacuation logistics represent one of the most challenging temporal‑optimization problems in emergency management. The system involves multiple dynamic components:
- Temporal patterns in fire spread (hourly/daily cycles, weather dependencies)
- Human‑behavior patterns (evacuation‑decision timing, route preferences)
- Infrastructure dynamics (road‑capacity degradation, communication‑network failures)
- Policy constraints (evacuation orders, resource‑allocation rules, jurisdictional boundaries)
During my investigation of existing evacuation models, I found that most approaches treated these components as independent or used simplified assumptions about their interactions. The breakthrough came when I started viewing the entire evacuation ecosystem as a temporal graph, where nodes represent decision points and edges represent temporal dependencies.
Through studying recent advances in self‑supervised learning, I learned that the key insight for temporal data is creating meaningful pre‑text tasks that force the model to learn useful representations. For evacuation networks, I developed three core pre‑text tasks:
- Temporal contrastive prediction – learning to distinguish between normal and anomalous temporal patterns.
- Masked temporal modeling – predicting missing segments of temporal sequences.
- Temporal alignment – learning to align patterns across different time scales.
One interesting finding from my experimentation with these tasks was that temporal contrastive learning produced the most robust representations for policy‑constrained scenarios. The model learned to recognize when temporal patterns violated policy constraints without explicit supervision.
My exploration of transformer architectures for temporal data led me to develop a hybrid model that combines temporal convolutional networks with attention mechanisms. The key innovation was incorporating policy constraints directly into the attention mechanism through constraint‑aware masking.
Policy‑Constrained Temporal Attention (PyTorch)
import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyConstrainedTemporalAttention(nn.Module):
def __init__(self, d_model, n_heads, max_seq_len=96):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
# Linear projections
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
# Policy‑constraint embeddings (10 constraint types)
self.policy_embedding = nn.Embedding(10, d_model)
# Positional embeddings for temporal information
self.temporal_position = nn.Embedding(max_seq_len, d_model)
def forward(self, x, policy_mask, temporal_positions):
"""
x : [batch, seq_len, d_model]
policy_mask : [batch, seq_len] (indices of constraint types)
temporal_positions: [batch, seq_len] (position indices)
"""
batch_size, seq_len, _ = x.shape
# Add temporal and policy information
x = x + self.temporal_position(temporal_positions)
x = x + self.policy_embedding(policy_mask)
# Multi‑head projections
Q = self.query(x).view(batch_size, seq_len, self.n_heads, self.head_dim)
K = self.key(x).view(batch_size, seq_len, self.n_heads, self.head_dim)
V = self.value(x).view(batch_size, seq_len, self.n_heads, self.head_dim)
# Scaled dot‑product attention
attn_scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) / (self.head_dim ** 0.5)
# Apply policy‑constraint mask (0 = forbidden)
policy_mask_matrix = self._create_policy_mask(policy_mask) # [batch, heads, seq_len, seq_len]
attn_scores = attn_scores.masked_fill(policy_mask_matrix == 0, float('-inf'))
attn_weights = F.softmax(attn_scores, dim=-1)
out = torch.einsum('bhqk,bkhd->bqhd', attn_weights, V)
out = out.reshape(batch_size, seq_len, self.d_model)
return out
def _create_policy_mask(self, policy_mask):
"""
Dummy implementation – replace with actual logic that creates a
[batch, heads, seq_len, seq_len] mask based on policy constraints.
"""
batch, seq_len = policy_mask.shape
mask = torch.ones(batch, self.n_heads, seq_len, seq_len, device=policy_mask.device)
# Example: zero‑out attention where constraint type == 0
mask[:, :, :, :] = (policy_mask.unsqueeze(1).unsqueeze(-1) != 0).float()
return maskTemporal Contrastive Loss (PyTorch)
class TemporalContrastiveLoss(nn.Module):
def __init__(self, temperature=0.1, temporal_window=6):
super().__init__()
self.temperature = temperature
self.temporal_window = temporal_window
def forward(self, embeddings, temporal_labels):
"""
embeddings : [batch, seq_len, embed_dim]
temporal_labels : [batch, seq_len] (segment identifiers)
"""
batch_size, seq_len, embed_dim = embeddings.shape
loss = 0.0
# Iterate over sliding windows to form anchor‑positive pairs
for i in range(seq_len - self.temporal_window):
# Anchor: mean embedding of the current window
anchor = embeddings[:, i:i + self.temporal_window].mean(dim=1) # [batch, embed_dim]
# Positive: mean embedding of the next window (temporal proximity)
positive = embeddings[:, i + 1:i + 1 + self.temporal_window].mean(dim=1)
# Compute cosine similarity
sim_pos = F.cosine_similarity(anchor, positive) / self.temperature
# Negatives: all other windows in the batch
neg_mask = torch.arange(seq_len - self.temporal_window) != i
negatives = embeddings[:, neg_mask][:, :, :].view(batch_size, -1, embed_dim)
neg_sim = F.cosine_similarity(
anchor.unsqueeze(1), negatives, dim=-1
) / self.temperature # [batch, num_neg]
# InfoNCE loss
logits = torch.cat([sim_pos.unsqueeze(1), neg_sim], dim=1) # [batch, 1+num_neg]
labels = torch.zeros(batch_size, dtype=torch.long, device=logits.device) # anchor is class 0
loss += F.cross_entropy(logits, labels)
return loss / (seq_len - self.temporal_window)Takeaway
Combining temporal contrastive learning with a policy‑aware attention mechanism yields representations that remain robust even when real‑time evacuation policies shift. This framework can be extended to other time‑critical, constraint‑driven domains such as flood response, pandemic logistics, and large‑scale power‑grid restoration.
Contrastive Loss Computation
# Positive: nearby temporal window
pos_start = i + self.temporal_window
pos_end = pos_start + self.temporal_window
positive = embeddings[:, pos_start:pos_end].mean(dim=1)
# Negatives: distant temporal windows
negative_indices = torch.randint(0, seq_len, (batch_size, 10))
negatives = embeddings[torch.arange(batch_size).unsqueeze(1),
negative_indices].mean(dim=1)
# Compute contrastive loss
pos_sim = F.cosine_similarity(anchor, positive, dim=-1)
neg_sim = F.cosine_similarity(anchor.unsqueeze(1), negatives, dim=-1)
logits = torch.cat([pos_sim.unsqueeze(1), neg_sim], dim=1) / self.temperature
labels = torch.zeros(batch_size, dtype=torch.long, device=embeddings.device)
loss += F.cross_entropy(logits, labels)
return loss / (seq_len - self.temporal_window)Differentiable Policy Layer (PyTorch)
class DifferentiablePolicyLayer(nn.Module):
def __init__(self, constraint_types, max_constraints=5):
super().__init__()
self.constraint_types = constraint_types
self.constraint_encoder = nn.Linear(constraint_types, 128)
self.temporal_projection = nn.Linear(128, 256)
def forward(self, temporal_patterns, policy_constraints, current_time):
"""
temporal_patterns: [batch_size, seq_len, features]
policy_constraints: [batch_size, num_constraints, constraint_dim]
current_time: scalar representing current time step
"""
batch_size, seq_len, _ = temporal_patterns.shape
# Encode policy constraints
constraint_emb = self.constraint_encoder(policy_constraints)
constraint_emb = torch.mean(constraint_emb, dim=1) # Aggregate constraints
# Project to temporal dimension
temporal_constraints = self.temporal_projection(constraint_emb)
temporal_constraints = temporal_constraints.unsqueeze(1).expand(-1, seq_len, -1)
# Apply constraints as attention modulation
constrained_patterns = temporal_patterns * torch.sigmoid(temporal_constraints)
# Time‑aware constraint enforcement
time_weights = self._compute_time_weights(current_time, seq_len)
constrained_patterns = constrained_patterns * time_weights.unsqueeze(-1)
return constrained_patterns
def _compute_time_weights(self, current_time, seq_len):
"""Compute weights based on temporal proximity to policy changes."""
time_steps = torch.arange(seq_len, device=self.constraint_encoder.weight.device)
time_diff = torch.abs(time_steps - current_time)
weights = torch.exp(-time_diff / 10.0) # Exponential decay
return weightsTemporal Route Optimizer (PyTorch)
class TemporalRouteOptimizer:
def __init__(self, pattern_miner, constraint_manager):
self.pattern_miner = pattern_miner
self.constraint_manager = constraint_manager
def optimize_evacuation_routes(self, current_state, time_horizon, policy_updates):
"""
current_state: Current evacuation network state
time_horizon: Number of future time steps to optimize
policy_updates: Real‑time policy changes
"""
# Extract temporal patterns from current state
temporal_features = self._extract_temporal_features(current_state)
# Apply policy constraints
constrained_features = self.constraint_manager.apply_constraints(
temporal_features, policy_updates
)
# Mine temporal patterns
patterns = self.pattern_miner.mine_patterns(constrained_features)
# Generate evacuation plans
plans = []
for t in range(time_horizon):
# Predict future states using learned patterns
future_state = self._predict_state(patterns, t)
# Optimize routes for this time step
routes = self._optimize_routes(future_state, policy_updates)
plans.append(routes)
# Update patterns based on new information
patterns = self._update_patterns(patterns, routes)
return plans
def _extract_temporal_features(self, state):
"""Extract temporal features from network state."""
features = []
# Road‑network temporal features
features.append(state['road_congestion_trend'])
features.append(state['evacuation_rate'])
features.append(state['resource_availability'])
# Environmental temporal features
features.append(state['fire_spread_rate'])
features.append(state['weather_conditions'])
return torch.stack(features, dim=-1)Policy Adaptive Attention (PyTorch)
class PolicyAdaptiveAttention(nn.Module):
def __init__(self, base_model, adaptation_layers=3):
super().__init__()
self.base_model = base_mod # Note: original variable name truncated; ensure correct reference
# Additional adaptation layers would be defined here
# ...
# Placeholder for further implementationAdaptive Model with Policy‑Conditioned Layers
self.adaptation_layers = nn.ModuleList([
nn.Linear(base_model.hidden_size, base_model.hidden_size)
for _ in range(adaptation_layers)
])
def forward(self, x, new_policy_constraints):
# Get base representations
base_repr = self.base_model(x)
# Rapid adaptation to new policies
adapted_repr = base_repr
for layer in self.adaptation_layers:
# Concatenate policy information
policy_expanded = new_policy_constraints.unsqueeze(1).expand(
-1, adapted_repr.size(1), -1
)
combined = torch.cat([adapted_repr, policy_expanded], dim=-1)
# Apply adaptation (residual connection)
adapted_repr = layer(combined) + adapted_repr
return adapted_reprSynthetic Evacuation Data Generation (PyTorch)
class SyntheticEvacuationGenerator:
def __init__(self, pattern_miner, physics_simulator):
self.pattern_miner = pattern_miner
self.physics_simulator = physics_simulator
def generate_scenarios(self, base_patterns, num_scenarios, variability=0.3):
"""Generate synthetic evacuation scenarios."""
scenarios = []
for _ in range(num_scenarios):
# Sample from learned patterns
pattern_idx = torch.randint(0, len(base_patterns), (1,))
base_pattern = base_patterns[pattern_idx]
# Apply realistic variations
varied_pattern = self._apply_variations(base_pattern, variability)
# Simulate physics‑based constraints
physics_constraints = self.physics_simulator.simulate(varied_pattern)
# Combine patterns with physics
full_scenario = self._combine_patterns(varied_pattern, physics_constraints)
scenarios.append(full_scenario)
return torch.stack(scenarios)Knowledge Distillation for Edge Deployment (PyTorch)
class TemporalKnowledgeDistillation:
def __init__(self, teacher_model, student_model, temperature=2.0):
self.teacher = teacher_model
self.student = student_model
self.temperature = temperature
self.criterion = nn.KLDivLoss(reduction='batchmean')
def distill(self, data_loader, optimizer, epochs=5):
self.teacher.eval()
self.student.train()
for epoch in range(epochs):
for batch in data_loader:
inputs = batch['inputs']
with torch.no_grad():
teacher_logits = self.teacher(inputs) / self.temperature
student_logits = self.student(inputs) / self.temperature
loss = self.criterion(
F.log_softmax(student_logits, dim=-1),
F.softmax(teacher_logits, dim=-1)
) * (self.temperature ** 2)
optimizer.zero_grad()
loss.backward()
optimizer.step()