AI-based Wildfire Prevention, Detection and Suppression System

• URL: http://arxiv.org/abs/2312.06990v1
• Date: Tue, 12 Dec 2023 05:18:23 GMT
• Title: AI-based Wildfire Prevention, Detection and Suppression System
• Authors: Prisha Shroff
• Abstract summary: Wildfire Prevention, Detection and Suppression System (WPDSS) is a novel, fully automated, end to end, AI based solution to effectively predict hotspots and detect wildfires. WPDSS will reduce the impacts of climate change, protect ecosystems and biodiversity, avert huge economic losses, and save human lives. The power of WPDSS developed can be applied to any location globally to prevent and suppress wildfires, reducing climate change.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wildfires pose a serious threat to the environment of the world. The global wildfire season length has increased by 19% and severe wildfires have besieged nations around the world. Every year, forests are burned by wildfires, causing vast amounts of carbon dioxide to be released into the atmosphere, contributing to climate change. There is a need for a system which prevents, detects, and suppresses wildfires. The AI based Wildfire Prevention, Detection and Suppression System (WPDSS) is a novel, fully automated, end to end, AI based solution to effectively predict hotspots and detect wildfires, deploy drones to spray fire retardant, preventing and suppressing wildfires. WPDSS consists of four steps. 1. Preprocessing: WPDSS loads real time satellite data from NASA and meteorological data from NOAA of vegetation, temperature, precipitation, wind, soil moisture, and land cover for prevention. For detection, it loads the real time data of Land Cover, Humidity, Temperature, Vegetation, Burned Area Index, Ozone, and CO2. It uses the process of masking to eliminate not hotspots and not wildfires such as water bodies, and rainfall. 2. Learning: The AI model consists of a random forest classifier, which is trained using a labeled dataset of hotspots and wildfires and not hotspots and not wildfires. 3. Identification of hotspots and wildfires: WPDSS runs the real time data through the model to automatically identify hotspots and wildfires. 4. Drone deployment: The drone flies to the identified hotspot or wildfire location. WPDSS attained a 98.6% accuracy in identifying hotspots and a 98.7% accuracy in detecting wildfires. WPDSS will reduce the impacts of climate change, protect ecosystems and biodiversity, avert huge economic losses, and save human lives. The power of WPDSS developed can be applied to any location globally to prevent and suppress wildfires, reducing climate change.

Related papers

• A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States [44.99833362998488]
  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.
  arXiv  Detail & Related papers  (2024-02-27T04:09:30Z)
• Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments [39.014859667729375]
  Wildfires pose a threat to life, property, ecology, cultural heritage, and critical infrastructure. SimFire is a versatile wildland fire projection simulator designed to generate realistic wildfire scenarios. SimHarness is a modular agent-based machine learning wrapper capable of automatically generating land management strategies.
  arXiv  Detail & Related papers  (2023-11-27T15:37:05Z)
• 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)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework [0.0]
  California's 2018 wildfire season caused damages of $148.5 billion. Among millions impacted people, those living with disabilities are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed.
  arXiv  Detail & Related papers  (2022-08-18T22:03:32Z)
• 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)
• Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks [0.0]
  Wildfires are uncontrolled fires in the environment that can be caused by humans or nature. In 2020 alone, wildfires in California have burned 4.2 million acres, damaged 10,500 buildings or structures, and killed more than 31 people. The objective of the research is to detect forest fires in their earlier stages to prevent them from spreading.
  arXiv  Detail & Related papers  (2021-09-10T19:30:37Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)
• EscapeWildFire: Assisting People to Escape Wildfires in Real-Time [1.978884131103313]
  This paper presents EscapeWildFire, a mobile application connected to a backend system which models and predicts wildfire geographical progression. A small pilot indicates the correctness of the system. The code is open-source; fire authorities around the world are encouraged to adopt this approach.
  arXiv  Detail & Related papers  (2021-02-23T08:58:37Z)
• 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.