A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States
- URL: http://arxiv.org/abs/2403.14657v1
- Date: Tue, 27 Feb 2024 04:09:30 GMT
- Title: A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States
- Authors: Stanley Chinedu Okoro, Alexander Lopez, Austine Unuriode,
- Abstract summary: This research investigates proactive methods for detecting and handling wildfires in the United States.
The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology.
Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management.
- Score: 44.99833362998488
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Over the past few years, wildfires have become a worldwide environmental emergency, resulting in substantial harm to natural habitats and playing a part in the acceleration of climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite improvements in detection techniques, the rising occurrence of wildfires demands creative solutions for prompt identification and effective control. This research investigates proactive methods for detecting and handling wildfires in the United States, utilizing Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. Utilizing advanced technology could save lives and prevent significant economic losses caused by wildfires. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.
Related papers
- A comprehensive survey of research towards AI-enabled unmanned aerial
systems in pre-, active-, and post-wildfire management [6.043705525669726]
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife.
Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management.
arXiv Detail & Related papers (2024-01-04T05:09:35Z) - AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation
Using Intelligent Sensing System [52.93806509364342]
This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology.
We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features.
We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
arXiv Detail & Related papers (2023-12-18T12:37:35Z) - An empirical study of automatic wildlife detection using drone thermal
imaging and object detection [6.179033141934765]
Recent advances in remotely piloted aircraft systems (RPAS or drones'') and thermal imaging technology have created new approaches to collect wildlife data.
We conduct a comprehensive review and empirical study of drone-based wildlife detection.
arXiv Detail & Related papers (2023-10-17T13:22:59Z) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z) - Improved Active Fire Detection using Operational U-Nets [18.786429304405097]
Using satellite imagery to monitor and detect active fires has been critical for managing forests and public land.
We propose a novel approach called Operational U-Nets for the improved early detection of active fires.
arXiv Detail & Related papers (2023-04-19T15:08:37Z) - Image-Based Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream.
To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
arXiv Detail & Related papers (2022-12-09T11:32:36Z) - Image-based Early Detection System for Wildfires [2.8494271563126676]
Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life.
We present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy.
Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily.
arXiv Detail & Related papers (2022-11-03T07:38:30Z) - Climate Change & Computer Audition: A Call to Action and Overview on
Audio Intelligence to Help Save the Planet [98.97255654573662]
This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges.
We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
arXiv Detail & Related papers (2022-03-10T13:32:31Z) - Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using
Meta-Learning [64.92447072894055]
Infrared (IR) cameras are robust under adverse illumination and lighting conditions.
We propose an algorithm meta-learning framework to improve existing UDA methods.
We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
arXiv Detail & Related papers (2021-10-07T02:28:18Z) - Unmanned Aerial Systems for Wildland and Forest Fires [0.0]
Wildfires represent an important natural risk causing economic losses, human death and important environmental damage.
Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting.
Unmanned Aerial Systems (UAS) have proven to be useful due to their maneuverability.
arXiv Detail & Related papers (2020-04-28T23:01:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.