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.
Hybrid deep learning model predicts urban floods with high accuracy.
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability…
Study improves flood loss risk models using historical data and rainfall data.
Framework improves ML flood mapping generalization.
Deep learning framework predicts streamflow and flood probabilities in Australian catchments.
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system provid…
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parame…
CNN model predicts fluvial floods quickly and accurately.
Paper proposes a novel approach to improve spatiotemporal precipitation forecasts.
Physically-based overland flow models are computationally demanding, hindering their use for real-time applications. Therefore, the development of fast (and reasonably accurate) overland flow models is needed if they are to be used to support flood mitigation decision making. In this study, we investigate the potential…
Study finds more flood risk strategies can improve outcomes in NYC.
Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This reco…
A new model predicts spatially varying inland flooding from time-varying inputs.
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex …
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore arti…
Overparameterized deep networks have the capacity to memorize training data with zero \emph{training error}. Even after memorization, the \emph{training loss} continues to approach zero, making the model overconfident and the test performance degraded. Since existing regularizers do not directly aim to avoid zero train…
SoftAD improves classification accuracy with less fine-tuning and fewer computational costs.
New protocol reduces communication costs for heterogeneous bandits over complex networks.
Forecast dam inflow using sea surface feature weights.
Machine learning predicts dam-break flood wave behavior accurately.
Neural network model forecasts extreme flood risk.
Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilu…
Low-cost water-level tracking using LTE power metrics and wavelet analysis.
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. M…
Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In thi…
New method provides reliable high-confidence prediction intervals for high-impact events.
Data scientists guide to streamflow prediction and flood forecasting.
The study models insurance dependence using Bernstein copulas.
Study optimizes climate adaptation strategies for NYC.
Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but …
Study optimal risk sharing in decentralized peer-to-peer markets with robust risk measures.
This paper compares unstructured and structured EM-based semi-supervised learning methods.
Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrological forecasts require models that are calibrated to past data. In this work we present a large-scale LSTM-based modeling approach that -- …
In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys…
Study tackles criterion collapse in learning criteria, showing conditions for loss minimization.
We consider a fundamental integer programming (IP) model for cost-benefit analysis flood protection through dike building in the Netherlands, due to Verweij and Zwaneveld. Experimental analysis with data for the Ijsselmeer lead to integral optimal solution of the linear programming relaxation of the IP model. This natu…
Modeling supply chain disruptions from climate hazards with adaptive firms.
HydroNets use river structure to improve hydrologic predictions.
The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to …
New method generates geolocated synthetic populations from real data.
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordi…
Novel Orlicz regrets consistently bound environmental variable statistics.
Persistent homology provides a new, efficient molecular descriptor for protein dynamics.
This paper uses VAE to generate extreme events from multivariate data.
Modeling daily river flow distribution with seasonal and long-term trends.
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning tec…