Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction

by   Sifat Chowdhury, et al.

Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ignition make the prediction task very challenging. In this paper, we develop a deep learning based data augmentation approach for wildfire risk prediction. We build a dataset consisting of diverse features responsible for fire ignition and utilize a conditional tabular generative adversarial network to explore the underlying patterns between the target value of risk levels and all involved features. For fair and comprehensive comparisons, we compare our proposed scheme with five other baseline methods where the former outperformed most of them. To corroborate the robustness, we have also tested the performance of our method with another dataset that also resulted in better efficiency. By adopting the proposed method, we can take preventive strategies of wildfire mitigation to reduce global GHG emissions.


page 1

page 5

page 6


Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions

In this paper, we present a crash frequency data augmentation method bas...

UnfairGAN: An Enhanced Generative Adversarial Network for Raindrop Removal from A Single Image

Image deraining is a new challenging problem in real-world applications,...

Unconstrained Road Marking Recognition with Generative Adversarial Networks

Recent road marking recognition has achieved great success in the past f...

On Data Augmentation and Adversarial Risk: An Empirical Analysis

Data augmentation techniques have become standard practice in deep learn...

Discriminative feature generation for classification of imbalanced data

The data imbalance problem is a frequent bottleneck in the classificatio...

Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU

Data sparsity is one of the key challenges associated with model develop...

Short-time SSVEP data extension by a novel generative adversarial networks based framework

Steady-state visual evoked potentials (SSVEPs) based brain-computer inte...

Please sign up or login with your details

Forgot password? Click here to reset