Editing Ai Climate

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AI for climate and sustainability applies artificial intelligence to monitor, understand, mitigate, and adapt to climate change and environmental challenges. Climate is one of the most data-rich, complex, and consequential domains in science — generating petabytes of satellite observations, ground sensors, climate model outputs, and socioeconomic data. AI offers tools to process this data at scale: improving weather forecasting, accelerating materials discovery for clean energy, optimizing energy grids, monitoring deforestation, and enabling precision agriculture. As climate change accelerates, AI is increasingly viewed as a necessary tool for both understanding and responding to its impacts.
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== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Climate model''' — A mathematical simulation of Earth's climate system; used to project future climate under different emissions scenarios.
* '''Climate emulator''' — A machine learning model that approximates the output of a physics-based climate model at much lower computational cost.
* '''GraphCast''' — Google DeepMind's AI weather forecasting system that achieved state-of-the-art 10-day forecasts; trained on 40 years of ERA5 reanalysis data.
* '''NowCasting''' — Very short-range weather prediction (0–6 hours ahead) at high resolution; AI surpasses numerical models here.
* '''Remote sensing''' — Using satellite or aerial sensors to observe Earth's surface, atmosphere, and oceans at scale.
* '''Deforestation monitoring''' — Using satellite imagery and computer vision to detect and quantify forest loss in near-real-time.
* '''Smart grid''' — An electricity grid that uses AI to balance supply and demand in real time, integrating variable renewable energy.
* '''Demand forecasting''' — Predicting future energy consumption to optimize generation and grid operations.
* '''Carbon footprint prediction''' — Using ML to estimate the carbon emissions associated with activities, products, or supply chains.
* '''CCUS (Carbon Capture, Utilization, and Storage)''' — Technologies for capturing CO₂ from the atmosphere or point sources; AI accelerates material discovery.
* '''Precision agriculture''' — Using sensors, satellites, and AI to optimize farming inputs (water, fertilizer, pesticide) and maximize yield while minimizing environmental impact.
* '''Biodiversity monitoring''' — Using AI to automatically identify species from audio (bird calls, bat echolocation) or imagery, enabling large-scale biodiversity surveys.
* '''Climate downscaling''' — Using ML to translate coarse-resolution climate model outputs to fine-resolution local projections.
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== <span style="color: #FFFFFF;">Understanding</span> ==
Climate and sustainability challenges are fundamentally data and computation problems that AI is well-suited to address. Several distinct AI application areas exist:

'''Weather and climate prediction''': Numerical weather prediction (NWP) simulates atmospheric physics by solving differential equations over a grid — expensive and slow. GraphCast learns the mapping from current atmospheric state to future states directly from historical data, achieving 10-day forecast accuracy matching ECMWF's model at ~1000× lower computational cost. Climate emulators do the same for century-scale climate projections.

'''Earth observation and monitoring''': Satellites generate terabytes of imagery daily. Computer vision models can automatically: detect deforestation (PlanetScope + segmentation models), track Arctic sea ice extent (MODIS + deep learning), map building damage after disasters, monitor methane leaks from oil wells, and estimate above-ground biomass for carbon accounting.

'''Energy system optimization''': Integrating intermittent renewables (solar, wind) into the grid requires accurate short-term forecasting and real-time balancing. ML models outperform persistence forecasts for solar and wind power; RL can optimize grid operations and battery storage dispatch. DeepMind's RL for cooling data centers reduced energy use by 40%.

'''Materials discovery for clean energy''': AI accelerates discovery of better solar cells, batteries, catalysts for green hydrogen, and CO₂ capture materials. GNNs for molecular property prediction and generative models for materials design dramatically reduce experimental search time.

'''The rebound effect caveat''': AI consumes significant energy itself. Large-scale AI training and inference can offset climate benefits if not powered by clean energy. The field of "sustainable AI" addresses this with efficient architectures, hardware, and green energy sourcing.
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== <span style="color: #FFFFFF;">Applying</span> ==
'''Satellite deforestation detection with segmentation:'''
<syntaxhighlight lang="python">
import torch
import segmentation_models_pytorch as smp
from torch.utils.data import DataLoader
from torchvision import transforms

# U-Net with ResNet-50 backbone for satellite imagery segmentation
model = smp.Unet(
    encoder_name="resnet50",
    encoder_weights="imagenet",
    in_channels=4,      # RGB + Near-Infrared (NIR) bands from Sentinel-2
    classes=2,          # Forest vs. deforested
)

# Training setup
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = smp.losses.DiceLoss(mode='binary')  # Better for imbalanced segmentation

# Inference on new satellite tile
def detect_deforestation(tile_tensor, threshold=0.5):
    """
    tile_tensor: (1, 4, H, W) satellite image with R, G, B, NIR bands
    Returns: binary mask of deforested areas
    """
    model.eval()
    with torch.no_grad():
        logits = model(tile_tensor)
        probs = torch.sigmoid(logits)
        return (probs > threshold).squeeze(0)  # (1, H, W) binary mask

# Computing deforestation area
def area_hectares(mask, pixel_size_m=10):  # Sentinel-2 = 10m/pixel
    """Convert binary mask to area in hectares."""
    n_pixels = mask.sum().item()
    return n_pixels * (pixel_size_m ** 2) / 10_000  # Convert m² to hectares
</syntaxhighlight>

; AI for climate: application landscape
: '''Weather forecasting''' → GraphCast, Pangu-Weather, FourCastNet, NeuralGCM
: '''Deforestation monitoring''' → Global Forest Watch (Hansen et al.) + daily alerts via Planet AI
: '''Energy demand forecasting''' → LightGBM, LSTM, TFT on smart meter data
: '''Solar/wind forecasting''' → XGBoost, LSTM, NWP post-processing
: '''Species identification''' → iNaturalist (CV), BirdNET (audio ML)
: '''Climate downscaling''' → Super-resolution CNNs on GCM output (ClimATE-ViT)
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== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ AI Climate Application Maturity
! Application !! AI Benefit !! Data Availability !! Deployment Readiness
|-
| Weather NowCasting || Very high || High (radar, satellite) || High (operational)
|-
| 10-day weather forecast || High || High || High (operational)
|-
| Deforestation monitoring || High || High (free satellite) || High (Global Forest Watch)
|-
| Solar/wind forecasting || High || Medium (site-specific) || High (utilities use it)
|-
| Climate emulation || High || Medium (model output) || Medium (research stage)
|-
| Materials discovery || High || Low (experimental) || Medium (research)
|-
| Species monitoring || High || Medium (growing) || Growing
|}

'''Failure modes and equity concerns''': Climate AI data is geographically biased — dense sensor networks in wealthy nations, sparse coverage in developing countries where climate impacts are worst. Models trained on historical climate data may fail during unprecedented climate events (the very events we need to predict). Remote sensing models trained in one biome (temperate forest) may fail in others (tropical). Energy optimization AI that reduces costs in wealthy areas may increase equity gaps.
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== <span style="color: #FFFFFF;">Evaluating</span> ==
Domain-specific evaluation:
# '''Weather''': ACC (Anomaly Correlation Coefficient) and RMSE on ERA5 reanalysis; compare at 500hPa geopotential height at 5, 7, 10 days — industry standard from WeatherBench2.
# '''Deforestation''': IoU (Intersection over Union) on held-out tiles across different biomes and time periods.
# '''Energy forecasting''': MAPE and pinball loss for probabilistic forecasts at multiple time horizons.
# '''Impact evaluation''': ultimately measured by actual policy outcomes — did better forecasts lead to better adaptation decisions?
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== <span style="color: #FFFFFF;">Creating</span> ==
Designing an AI climate monitoring system:
# Data ingestion: acquire Sentinel-2 (ESA, free) and Planet Labs imagery for target region via API.
# Pre-processing: cloud masking, atmospheric correction, band normalization.
# Change detection model: U-Net trained on labeled deforestation patches; retrained quarterly on new labels.
# Alert system: flag tiles with deforestation probability >0.85 for analyst review within 48 hours of image acquisition.
# Dashboard: geospatial visualization of detected change, cumulative area affected, trend analysis.
# Integration: feed alerts to government enforcement agencies and carbon credit registries.

[[Category:Artificial Intelligence]]
[[Category:Climate]]
[[Category:Sustainability]]
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