Deep learning aids UAVs in identifying disasters with high accuracy.
problem Monitoring disasters for effective mitigation.
method Deep learning applied to aerial photos from UAVs.
result 91% accuracy in disaster identification from 544 images.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “disaster monitoring”
A scheme for UAVs to borrow spectrum from terrestrial networks for disaster relief.
problem Spectrum shortage in UAV networks for critical missions.
method Hierarchical spectrum sharing model with relaying UAVs and reinforcement learning.
result Autonomous spectrum management improves QoS and prolongs UAV lifetime.
Social media analysis improves disaster situational awareness.
problem Limited traditional methods for disaster SA.
method Text mining methods like sentiment and topic modeling.
result TwiSA framework effectively tracks negative concerns during disasters.
Paper examines how income support affects retirement decisions for low-income individuals.
problem Retirement decisions of low-income individuals affected by income disaster.
method Modeling consumption/savings, investment, and retirement choices with income support.
result Optimal retirement decision depends on the level of income support provided.
Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Dataq-fin.ST
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
A Natural Disasters Indexq-fin.RM
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
Paper presents a model for identifying informative COVID-19 tweets.
problem Identifying informative COVID-19 tweets on Twitter.
method Leveraged transformers (RoBERTa, XLNet, BERTweet) trained in Semi-Supervised Learning (SSL) setting.
result Achieved F1 score of 0.9011 on test set, ranking 7th on leaderboard.
Study proposes a tax-based system to share disaster risk among regions.
problem Systemic risk in catastrophic events and insurer insolvency.
method Public-private partnership with government intervention through taxation.
result Taxation system effectively shares residual claims in case of insurer insolvency.
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detectorcs.LG
Paper improves disaster damage detection using modern transfer learning.
problem Harder to obtain damaged examples than undamaged ones.
method Revisits classical bagging with modern transfer learning.
result Significantly outperforms existing methodologies.
Bayesian model uses mobile data to assess business resilience after hurricanes.
problem Evaluating economic impact of extreme shocks on businesses.
method Bayesian structural time series model with mobile phone data.
result Estimates business resilience after hurricanes, revealing key characteristics.
Deep learning enhances water resources management through data analysis.
problem Data volume and variety in water resources management.
method Systematic review of deep learning applications in hydrology and water resources.
result Deep learning improves water resources monitoring, prediction, and classification.
Machine learning detects building damage in satellite images.
problem Extracting damage information from satellite imagery is slow and labor-intensive.
method Used four convolutional neural network models to detect damaged buildings.
result Models performed well in detecting damaged buildings in the 2010 Haiti earthquake.
This paper improves typhoon intensity prediction using social media data and semantic word embeddings.
problem Short-term disaster prediction from historical data alone is limited.
method Combining semantically-enriched word embeddings with traditional word2vec for social media data, and an end-to-end learning framework.
result Our approach outperforms state-of-the-art baselines in typhoon intensity prediction.
Proposes a new Hawkes process bandit model for disaster search and rescue.
problem Forecasting and detecting spatio-temporal events with undersampled or biased data.
method Upper confidence bound algorithm using Bayesian spatial Hawkes process estimation.
result Model outperforms state-of-the-art spatial MAB algorithms in disaster search and rescue.
Compact satellite tech detects flooded areas quickly.
problem Rapid access to satellite data and automatic flood detection.
method Automatically identifies cloud, water, and land in satellite images onboard small satellites.
result Reduced downlink bandwidth by 2 orders of magnitude.
ResQ uses reinforcement learning to optimize rescue efforts in disaster scenarios.
problem Optimizing emergency response in urban disaster zones.
method Heuristic multi-agent reinforcement learning scheduling algorithm.
result ResQ outperforms state-of-the-art methods in rescue efficiency.
Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Featurecs.CV
This paper classifies typhoon damage features using aerial photography.
problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.
Unified framework for growth models with environmental risk and pollution-dependent disasters.
problem Analyzing how rare but catastrophic shocks interact with capital accumulation and pollution in stochastic growth models.
method General Poisson point process formulation leading to non-local HJB equations with closed-form solutions.
result Unified framework captures how environmental degradation amplifies macroeconomic vulnerability and strengthens incentives for abatement.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
DASC combines social media and car sensors to improve disaster response.
problem Inconsistent reliability and inconsistent availability of human sensors.
method Hybrid social-car sensing system using game theory, feedback control, and MDP.
result DASC improves detection accuracy and efficiency in disaster response.
FAL improves formation resistivity prediction from cased boreholes with noise resistance.
problem Noise and high-frequency disaster in predicting formation resistivity from cased boreholes.
method Frequency-aware framework and temporal anti-noise block for LSTM.
result FAL achieves a 24.3% improvement in R2 over LSTM, reaching R2=0.91.
Graph Attention Networks predict power outage durations from natural disasters.
problem Accurately predicting power outage durations from geospatial and weather data.
method Graph Attention Networks (GAT) for semi-supervised learning.
result GAT model outperforms existing methods by 2% - 15% in accuracy.
BCCNet combines biased crowd labels to train classifiers for disaster response.
problem Improper labels from citizen scientists limit machine learning applications.
method Bayesian classifier combination neural network (BCCNet) aggregates and trains classifiers from imperfect labels.
result BCCNet effectively processes large unstructured data for disaster prevention and response.
I-AID categorizes disaster tweets into useful information types.
problem Filtering useful information from social media during disasters.
method Multimodel approach using BERT, GAT, and Relation Network.
result I-AID outperforms state-of-the-art approaches in F1 scores.
Nostradamus: Weathering Worthq-fin.ST
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
Deep learning model classifies tweets for disaster rescue scheduling.
problem Efficiently processing and categorizing tweets for disaster rescue.
method Combining attention-based Bi-directional LSTM and CNN with pre-trained word vectors for classification and feature engineering.
result Proposed model outperforms other methods in Precision, Recall, F1-score, and Accuracy.
Paper detects important sub-events in disaster tweets.
problem Identifying useful information during large-scale disasters.
method Extract noun-verb pairs, learn semantic embeddings, rank and cluster.
result Effective unsupervised learning framework for sub-event detection.
Few-shot models detect tweets in emerging disasters efficiently.
problem Detecting relevant tweets in emerging disaster events is challenging.
method Few-shot models (matching networks and prototypical networks) are used to detect tweets in emerging disaster events.
result Few-shot models can generalize to unseen classes with a small amount of examples.
Bayesian approach for adaptive radio tomography using SLFs.
problem Accurately modeling spatial loss fields for interference management.
method Variational Bayes framework with hidden Markov random field model.
result Efficient field estimators at reduced complexity.
Deep learning predicts bridge load capacity from images.
problem Lack of data on aging bridges in post-disaster zones.
method Crowd-sourced images trained on a new CNN for multiclass classification.
result Improved prediction accuracy and practical optimisation.
Machine learning aids in climate change mitigation and adaptation.
problem Reducing greenhouse gas emissions and societal adaptation to climate change.
method Identifying high-impact problems and leveraging machine learning in collaboration with other fields.
result Machine learning can fill existing gaps in climate change solutions.
Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations…
Deep learning speeds up infrastructure reliability analysis.
problem Efficiently evaluate infrastructure system reliability under natural disasters.
method Developed deep neural network surrogates for connectivity determination and system reliability analysis.
result Proposed approach accelerates reliability analysis with high accuracy.
Study uses SVM to predict weather-induced home insurance claims and losses.
problem Assessing future weather-induced home insurance claims and losses for disaster preparedness.
method Support Vector Machine (SVM) regression for forecasting future claim dynamics.
result Illustrates SVM approach in forecasting weather-induced home insurance claims in a Canadian city.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Bayesian approach for aggregating unreliable data sources to create accurate heatmaps.
problem Classifying regions with sparse, unreliable data from multiple sources.
method Bayesian Gaussian Process classifier that models reliability and bias of each data source.
result Reduces crowdsourced data needed and improves accuracy of heatmaps.
Modeling complex systems with multi-resolution data and causal dependencies.
problem Accurate prediction of complex systems with varying causal dependencies and multi-resolution data.
method Score-based Variational Graphical Diffusion Model (Temporal-SVGDM) that constructs individual SDEs for each variable at its native resolution and couples them through a causal score mechanism.
result Improved prediction accuracy and causal understanding compared to existing methods, especially in temporal scenarios.
Proposes a test for shared information between time series and events.
problem Detecting extreme events in time series data.
method Non-parametric statistical test using multiple two-sample testing at increasing lags.
result Outperforms or matches related tests on various datasets.
Paper proposes a framework to analyze DV on social media.
problem Lack of actionable knowledge from DV social media data.
method Develops a novel framework to model and discover themes related to DV.
result Provides actionable knowledge from DV social media content.
The study examines how formal index insurance compares to informal risk sharing in managing natural disasters.
problem The challenges of natural disasters and the effectiveness of index insurance in risk management.
method A three-strategy evolutionary game model to analyze the competitive relationship between formal index insurance, informal risk sharing, and non-insurance.
result Basis risk and loss ratio significantly impact the adoption rate of index insurance, with different strategies preferred under varying conditions.
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
problem Severe rise in magnitude and frequency of floods causing catastrophic losses.
method Extremes analysis using Peaks-Over-Threshold method and Point Process model; Value-at-Risk (VaR) and Conditional VaR (CVaR) estimation; Flood zoning insurance and catastrophic bond design.
result Developed flood risk vulnerability and threat analysis considering geography and economic factors; Proposed flood zoning insurance and catastrophic bond design.
We develop a simple test for deviations from power law tails, which is based on the asymptotic properties of the empirical distribution function. We use this test to answer the question whether great natural disasters, financial crashes or electricity price spikes should be classified as dragon kings or 'only' as black…
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learningcs.LG
Prototype for early fault warnings in large electric grids.
problem Early detection and classification of faults in complex electric grids.
method Multi-stage approach with anomaly detection, feature mapping, classification, and clustering.
result Random forest method offers the most accurate fault classification.
This study tackles basis risk in weather parametric insurance using Monte Carlo simulations.
problem Mismatch between actual loss and payout in weather parametric insurance leads to loss without payout or payout without loss.
method Empirical research using Monte Carlo simulations to test diversification and hedging strategies.
result Portfolio basis risk and volatility decrease with more contracts, and spatial relationships significantly impact basis risk.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
Spatially-aware model improves earthquake hazard assessment accuracy.
problem Misrepresentation of seismic effects across diverse landscapes.
method Causal Bayesian network with Gaussian Processes and normalizing flows.
result Achieves up to 35.2% AUC improvement over existing methods.
AI systems that explain their decisions can be monitored for harmful intentions.
problem Monitoring AI systems' decision-making processes for harmful intentions is imperfect and can miss some misbehavior.
method Monitoring the chain of thought (CoT) of AI systems that communicate in human language.
result CoT monitoring is a promising but fragile approach to AI safety.
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.