WildfireGPT: Tailored Large Language Model for Wildfire Analysis
- URL: http://arxiv.org/abs/2402.07877v1
- Date: Mon, 12 Feb 2024 18:41:55 GMT
- Title: WildfireGPT: Tailored Large Language Model for Wildfire Analysis
- Authors: Yangxinyu Xie, Tanwi Mallick, Joshua David Bergerson, John K.
Hutchison, Duane R. Verner, Jordan Branham, M. Ross Alexander, Robert B.
Ross, Yan Feng, Leslie-Anne Levy, Weijie Su
- Abstract summary: WildfireGPT is a prototype agent designed to transform user queries into actionable insights on wildfire risks.
We enrich WildfireGPT by providing additional context such as climate projections and scientific literature to ensure its information is current, relevant, and scientifically accurate.
This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users.
- Score: 9.71637542445321
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The recent advancement of large language models (LLMs) represents a
transformational capability at the frontier of artificial intelligence (AI) and
machine learning (ML). However, LLMs are generalized models, trained on
extensive text corpus, and often struggle to provide context-specific
information, particularly in areas requiring specialized knowledge such as
wildfire details within the broader context of climate change. For
decision-makers and policymakers focused on wildfire resilience and adaptation,
it is crucial to obtain responses that are not only precise but also
domain-specific, rather than generic. To that end, we developed WildfireGPT, a
prototype LLM agent designed to transform user queries into actionable insights
on wildfire risks. We enrich WildfireGPT by providing additional context such
as climate projections and scientific literature to ensure its information is
current, relevant, and scientifically accurate. This enables WildfireGPT to be
an effective tool for delivering detailed, user-specific insights on wildfire
risks to support a diverse set of end users, including researchers, engineers,
urban planners, emergency managers, and infrastructure operators.
Related papers
- Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning [87.10396098919013]
Large Language Models (LLMs) have demonstrated extensive knowledge and remarkable proficiency in temporal reasoning.
We propose a Large Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on Temporal Knowledge Graphs.
LLM-DA harnesses the capabilities of LLMs to analyze historical data and extract temporal logical rules.
arXiv Detail & Related papers (2024-05-23T04:54:37Z) - Decision support system for Forest fire management using Ontology with Big Data and LLMs [0.8668211481067458]
Fire weather indices, which assess wildfire risk and predict resource demands, are vital.
With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are increasingly used to gather climatic data.
This paper discusses using Apache Spark for early forest fire detection, enhancing fire risk prediction with meteorological and geographical data.
arXiv Detail & Related papers (2024-05-18T17:30:30Z) - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems.
We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
arXiv Detail & Related papers (2024-05-07T04:00:30Z) - Explainable AI Integrated Feature Engineering for Wildfire Prediction [1.7934287771173114]
We conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires.
For classifying different types or stages of wildfires, the XGBoost model outperformed others in terms of accuracy and robustness.
The Random Forest regression model showed superior results in predicting the extent of wildfire-affected areas.
arXiv Detail & Related papers (2024-04-01T21:12:44Z) - Knowledge Plugins: Enhancing Large Language Models for Domain-Specific
Recommendations [50.81844184210381]
We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
arXiv Detail & Related papers (2023-11-16T07:09:38Z) - Forgery-aware Adaptive Vision Transformer for Face Forgery Detection [57.56537940216884]
We propose a Forgery-aware Adaptive Vision Transformer (FA-ViT)
In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge.
Two specially designed components, the Local-aware Forgery (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge.
arXiv Detail & Related papers (2023-09-20T06:51:11Z) - Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models.
The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies.
We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
arXiv Detail & Related papers (2023-07-03T16:01:45Z) - On the Uses of Large Language Models to Interpret Ambiguous Cyberattack
Descriptions [1.6317061277457001]
Tactics, Techniques, and Procedures (TTPs) are to describe how and why attackers exploit vulnerabilities.
A TTP description written by one security professional can be interpreted very differently by another, leading to confusion in cybersecurity operations.
Advancements in AI have led to the increasing use of Natural Language Processing (NLP) algorithms to assist the various tasks in cyber operations.
arXiv Detail & Related papers (2023-06-24T21:08:15Z) - 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) - Assimilation of Satellite Active Fires Data [0.0]
The aim of this thesis is to develop techniques to help combat the impacts of wildfires by improving wildfire modeling capabilities.
In particular, we develop a method for constructing the history of a fire, a new technique for assimilating wildfire data, and a method for modifying the behavior of a modeled fire.
arXiv Detail & Related papers (2022-04-01T20:11:28Z) - A review of machine learning applications in wildfire science and
management [1.7322441975875131]
We present a scoping review of machine learning (ML) in wildfire science and management.
Our objective is to improve awareness of ML among wildfire scientists and managers.
arXiv Detail & Related papers (2020-03-02T03:59:38Z)
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.