Impacts of extreme weather events on mortgage risks and their evolution under climate change:A case study on Florida

International audienceWe develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios

Deep learning for Digital Typhoon: Exploring a typhoon satellite image dataset using deep learning

Exploring Deep Learning techniques to provide Emergency response using Deep Learning TechniquesEfficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several different approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset "Digital Typhoon". We focus on intensity measurement and categorisation of different natural phenomena. Firstly, we build a classifier to differentiate natural tropical cyclones and extratropical cyclones and, secondly, we implement a regression model to estimate the centre pressure value of a typhoon. In addition, we also explore cleaning methodologies to ensure that the data used is reliable. The results obtained show that deep learning techniques can be effective under certain circumstances, providing reliable classification/regression models and feature extractors. More research to draw more conclusions and validate the obtained results is expected in the future.Els sistemes d'alerta ràpida poden ajudar en la gestió dels esdeveniments de desastres naturals, permetent una evacuació i administració dels recursos adequada. En aquest sentit s'han utilitzat diferentes tècniques per implementar sistemes d'alerta, com ara simulacions o models estadístics, tots ells basats en la recollida de dades meteorològiques. S'ha demostrat que les tècniques basades en dades són eficaces a l'hora de construir models estadístics, podent generalitzar-se a a noves dades. Motivat per això, en aquest treball, explorem l'ús de tècniques d'apre-nentatge profund (o \emph{deep learning}) aplicades a les imatges meteorològi-ques per satèl·lit de tifons del projecte "Digital Typhoon". Ens centrem en la mesura i la categorització de la intensitat de diferents fenòmens naturals. En primer lloc, construïm un classificador per diferenciar ciclons tropicals naturals i ciclons extratropicals i, en segon lloc, implementem un model de regressió per estimar el valor de pressió central d'un tifó. A més, també explorem metodologies de neteja per garantir que les dades utilitzades siguin fiables.   Els resultats obtinguts mostren que les tècniques d'aprenentatge profundes poden ser efectives en determinades circumstàncies, proporcionant models fiables de classificació/regressió i extractors de característiques. Es preveu que hi hagi més recerques per obtenir més conclusions i validar els resultats obtinguts en el futur

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November 11-16, 2018, Dallas, TX, US

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Generalised additive point process models for natural hazard occurrence

ArticleThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Point processes are a natural class of model for representing occurrences of various types of natural hazard event. Flexibly implementing such models is often hindered by intractable likelihood forms. Consequently, rates of point processes tend to be reduced to parametric forms, or the processes are discretised to give data of readily modelled `count-per-unit' type. This work proposes generalised additive model forms for point process rates. The resulting low-rank spatio-temporal representations of rates, coupled with the Laplace approximation, makes the restricted likelihood relatively tractable, and hence inference for such models possible. The models can also be interpreted from a regression perspective. The proposed models are used to estimate di erent types of Cox process and then spatio-temporal variation in European windstorms. Through a combination of thin plate and cubic regression splines, and their tensor product, established relationships between where windstorms occur and the state of the North Atlantic Oscillation are con rmed, and then expanded to bring detailed understanding of within-year variation, which has otherwise not been possible with count-based models.Willis Research Networ

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC