Model Accuracy
94.7%
+2.1% from last epoch
Validation Loss
0.023
Converging steadily
Training Samples
2.3M
Across 8 data sources
Active Experiments
47
12 running, 35 queued
Machine Learning Pipeline
v2.4.1
Random Forest
XGBoost
Neural Network
SVM
1# Fire Risk Prediction Model - Random Forest
2import pandas as pd
3import numpy as np
4from sklearn.ensemble import RandomForestRegressor
5from sklearn.model_selection import train_test_split
6
7# Load wildfire dataset
8df = pd.read_csv('fire_data.csv')
9features = ['temperature', 'humidity', 'wind_speed',
10 'vegetation_index', 'elevation', 'slope']
11
12X = df[features]
13y = df['fire_risk_score']
14
15X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
16
17model = RandomForestRegressor(n_estimators=100)
18model.fit(X_train, y_train)
19print(f"R²: {model.score(X_test, y_test):.4f}")
Feature Importance Analysis
Experiment Notebook
experiment_042.ipynb
In [1]
Python 3.11
# Seasonal fire pattern analysis
import pandas as pd
fires = pd.read_parquet('fire_events_2019_2025.parquet')
monthly = fires.groupby(fires.date.dt.month).agg({
'acres_burned': 'sum',
'incidents': 'count'
})
print(monthly.describe())
import pandas as pd
fires = pd.read_parquet('fire_events_2019_2025.parquet')
monthly = fires.groupby(fires.date.dt.month).agg({
'acres_burned': 'sum',
'incidents': 'count'
})
print(monthly.describe())
Out [1]
Analysis complete. Peak fire activity July-September.Avg monthly incidents: 1,247 | Peak: 4,892 (August)
Total acres burned 2019-2025: 18.7M acres
In [2]
Python 3.11
# Fire spread simulation
from climaiq.models import SpreadSimulator
sim = SpreadSimulator(
wind_speed=25, humidity=15,
fuel_moisture=8.2, terrain='chaparral'
)
result = sim.run(hours=24)
print(result.summary())
from climaiq.models import SpreadSimulator
sim = SpreadSimulator(
wind_speed=25, humidity=15,
fuel_moisture=8.2, terrain='chaparral'
)
result = sim.run(hours=24)
print(result.summary())
Out [2]
Projected 2,400 acres in 24h. Peak rate: 150 acres/hour.Wind-driven spread NE at 2.3 km/h. Containment probability: 34%.
Recommended resource deployment: 6 crews, 4 air tankers.
Experiment Log
auto-refreshing
14:32:07 [INFO] Initializing Random Forest training pipeline...
14:32:08 [INFO] Loaded 2,341,876 training samples from data lake
14:32:12 [INFO] Feature engineering: 20 features extracted, 6 selected
14:33:45 [INFO] Epoch 1/50 complete. Loss: 0.087, Acc: 0.891
14:35:22 [WARN] GPU memory usage at 89% - consider batch reduction
14:38:01 [INFO] Epoch 25/50 complete. Loss: 0.031, Acc: 0.942
14:41:19 [INFO] Training complete. Final R²: 0.947. Model saved to registry.
Research Datasets
7 sources
-
847 MBHistorical Fire Data
-
StreamingWeather Station Network
-
12.7 TBSatellite Imagery
-
2.1 GBVegetation Health Index
-
89 MBLive Fuel Moisture
-
4.8 GBDigital Elevation Models
-
245 MBSocioeconomic Risk Factors
Research Tools
Data Mining
Statistical Analysis
Geospatial
Time Series
FireBench Integration
Google Research
# Google Research FireBench - Physics-Informed Model
import tensorflow as tf
from firebench import LESModel, PhysicsLoss
model = LESModel(
resolution=30, # meters
physics_constraints=True,
fuel_model='anderson_13'
)
result = model.simulate(
ignition_point=(34.05, -118.24),
duration_hours=48
)
import tensorflow as tf
from firebench import LESModel, PhysicsLoss
model = LESModel(
resolution=30, # meters
physics_constraints=True,
fuel_model='anderson_13'
)
result = model.simulate(
ignition_point=(34.05, -118.24),
duration_hours=48
)