Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification

• URL: http://arxiv.org/abs/2506.23462v1
• Date: Mon, 30 Jun 2025 01:56:05 GMT
• Title: Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification
• Authors: Manaswi Kulahara, Gautam Siddharth Kashyap, Nipun Joshi, Arpita Soni,
• Abstract summary: DisasterNet-LLM is a specialized Large Language Model (LLM) designed for comprehensive disaster analysis.<n>By leveraging advanced pretraining, cross-modal attention mechanisms, and adaptive transformers, DisasterNet-LLM excels in disaster classification.
• Score: 0.46873264197900916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective disaster management requires timely and accurate insights, yet traditional methods struggle to integrate multimodal data such as images, weather records, and textual reports. To address this, we propose DisasterNet-LLM, a specialized Large Language Model (LLM) designed for comprehensive disaster analysis. By leveraging advanced pretraining, cross-modal attention mechanisms, and adaptive transformers, DisasterNet-LLM excels in disaster classification. Experimental results demonstrate its superiority over state-of-the-art models, achieving higher accuracy of 89.5%, an F1 score of 88.0%, AUC of 0.92%, and BERTScore of 0.88% in multimodal disaster classification tasks.

Related papers

• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management [21.721973352020935]
  DMRetriever is a first series of dense retrieval models tailored for disaster management scenarios.<n>It achieves state-of-the-art (SOTA) performance across all six search intents at every model scale.<n>DMRetriever is highly parameter-efficient, with 596M model outperforming baselines over 13.3 X larger and 33M model exceeding baselines with only 7.6% of their parameters.
  arXiv  Detail & Related papers  (2025-10-16T19:08:34Z)
• Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management [50.34345101758248]
  We propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions.<n>Our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency.<n>Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
  arXiv  Detail & Related papers  (2025-02-25T16:15:35Z)
• Predictive Crash Analytics for Traffic Safety using Deep Learning [0.0]
  This research presents an innovative approach to traffic safety analysis through the integration of ensemble learning methods and multi-modal data fusion.<n>Our primary contribution lies in developing a hierarchical severity classification system that combines spatial-temporal crash patterns with environmental conditions.<n>We introduce a novel feature engineering technique that integrates crash location data with incident reports and weather conditions, achieving 92.4% accuracy in risk prediction and 89.7% precision in hotspot identification.
  arXiv  Detail & Related papers  (2025-02-09T05:00:46Z)
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response [50.76124284445902]
  Building damage assessment (BDA) is an essential capability in the aftermath of a disaster to reduce human casualties.<n>Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events.<n>We present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
  arXiv  Detail & Related papers  (2025-01-10T14:57:18Z)
• A Knowledge-Informed Large Language Model Framework for U.S. Nuclear Power Plant Shutdown Initiating Event Classification for Probabilistic Risk Assessment [6.200373536860575]
  We propose a hybrid pipeline that integrates a knowledge-informed machine learning mode to prescreen non-SDIEs and a large language model (LLM) to classify SDIEs into four types. The proposed approaches are evaluated on a dataset with 10,928 events using precision, recall ratio, F1 score, and average accuracy.
  arXiv  Detail & Related papers  (2024-09-30T20:35:03Z)
• CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [50.122541222825156]
  This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM)<n>Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM.<n>This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
  arXiv  Detail & Related papers  (2024-06-16T23:01:10Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models [21.327960186900885]
  This study develops linear regression models capable of predicting seismic intensity distributions based on earthquake parameters. The dataset comprises seismic intensity data from earthquakes that occurred in the vicinity of Japan between 1997 and 2020. The proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.
  arXiv  Detail & Related papers  (2024-02-03T13:39:22Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank [52.20298962359658]
  In crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. Semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others. We propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training.
  arXiv  Detail & Related papers  (2023-10-23T05:25:51Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief [2.007385102792989]
  We propose a multimodal framework (GaLeNet) for assessing the severity of damage by complementing pre-disaster images with weather data and the trajectory of the hurricane. We show that GaLeNet can leverage pre-disaster images in the absence of post-disaster images, preventing substantial delays in decision making.
  arXiv  Detail & Related papers  (2022-06-18T16:45:57Z)
• A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs [0.0]
  One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
  arXiv  Detail & Related papers  (2021-07-29T18:22:34Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)

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.