Evaluation of GPU Acceleration for WRF–SFIRE

WRF–SFIRE is an open source, atmospheric–wildfire model that couples the WRF model with the level set fire spread model to simulate wildfires in real time. This model has many applications and more scientific questions can be asked and answered if the model can be run faster. Nvidia has put a lot of effort into easing the barrier of entry for accelerating applications with their tools to be run on GPUs. Various physical simulations have been successfully ported to utilize GPUs and have benefited from the speed increase. In this research, we take a look at WRF-SFIRE and try to use the Nvida tools to accelerate portions of code. We were successful in offloading work to the GPU. However, the WRF-SFIRE codebase contains too many data dependencies, deeply nested function calls and I/O to effectively utilize the GPU’s resources. We look at specific examples and try to run them on a Titan V GPU. In the end, the compute intensive portions of WRF-SFIRE need to be rewritten to avoid data dependencies in order to leverage GPUs to improve the execution time

Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. Therefore, this study develops and tests a new methodological framework for modeling trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. The proposed methodology aims at forecasting evacuation trips and other types of trips. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested in this study for a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are evacuation order/warning information, proximity to fire, and population change, which are consistent with behavioral theories and empirical findings

Critical Market Crashes

This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distribution of drawdowns, or runs of successive losses shows that large financial crashes are ``outliers'': they form a class of their own as can be seen from their statistical signatures. If large financial crashes are ``outliers'', they are special and thus require a special explanation, a specific model, a theory of their own. In addition, their special properties may perhaps be used for their prediction. The main mechanisms leading to positive feedbacks, i.e., self-reinforcement, such as imitative behavior and herding between investors are reviewed with many references provided to the relevant literature outside the confine of Physics. Positive feedbacks provide the fuel for the development of speculative bubbles, preparing the instability for a major crash. We demonstrate several detailed mathematical models of speculative bubbles and crashes. The most important message is the discovery of robust and universal signatures of the approach to crashes. These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets, on currency markets, on company stocks, and so on. The concept of an ``anti-bubble'' is also summarized, with two forward predictions on the Japanese stock market starting in 1999 and on the USA stock market still running. We conclude by presenting our view of the organization of financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Potential changes in forest composition could reduce impacts of climate change on boreal wildfires

There is general consensus that wildfires in boreal forests will increase throughout this century in response to more severe and frequent drought conditions induced by climate change. However, prediction models generally assume that the vegetation component will remain static over the next few decades. As deciduous species are less flammable than conifer species, it is reasonable to believe that a potential expansion of deciduous species in boreal forests, either occurring naturally or through landscape management, could offset some of the impacts of climate change on the occurrence of boreal wildfires. The objective of this study was to determine the potential of this offsetting effect through a simulation experiment conducted in eastern boreal North America. Predictions of future fire activity were made using multivariate adaptive regression splines (MARS) with fire behavior indices and ecological niche models as predictor variables so as to take into account the effects of changing climate and tree distribution on fire activity. A regional climate model (RCM) was used for predictions of future fire risk conditions. The experiment was conducted under two tree dispersal scenarios: the status quo scenario, in which the distribution of forest types does not differ from the present one, and the unlimited dispersal scenario, which allows forest types to expand their range to fully occupy their climatic niche. Our results show that future warming will create climate conditions that are more prone to fire occurrence. However, unlimited dispersal of southern restricted deciduous species could reduce the impact of climate change on future fire occurrence. Hence, the use of deciduous species could be a good option for an efficient strategic fire mitigation strategy aimed at reducing fire propagation in coniferous landscapes and increasing public safety in remote populated areas of eastern boreal Canada under climate change

An Assessment of the Viability of LandScan Data to Estimate Structure Location in Wildland Fire Management and Planning

Research indicates firefighting costs in the wildland-urban interface (WUI) are highly correlated with the number of homes threatened by wildfire. Therefore, knowing the location of structures is paramount for planners and fire managers attempting to reduce the threats posed to structures by wildfire, and for the attainment of land management goals and objectives for reducing hazardous fuels surrounding them. Yet, no national-level structure location dataset exists. Previous attempts, such as the SILVIS Lab’s product, to predict structure location and the extent of the WUI have relied on Census block-level data. While urban Census blocks are generally small in area, those corresponding to sparsely settled areas may contain many square miles of territory. Rural Census blocks can contain small clusters of homes in one area, but any large uninhabited regions in the remaining area can result in an average structure density that is lower than the federal WUI criteria. Additionally, the designation of an entire large Census block as WUI, when only a small portion of the block contains houses, simultaneously causes both an underestimation in the number of Census blocks that contain areas meeting the density criterion and overestimates the extent of the WUI. LandScan USA, created by researchers at the Oak Ridge National Laboratory, estimates the population distribution for the United States using Census blocklevel housing data and additional inputs including transportation infrastructure, land cover, elevation, and cultural criterion, such as recreational features, retail establishments, employment, and educational locations. In order to test the accuracy of the LandScan USA dataset for predicting structure locations in the WUI, this study measures the spatial coincidence between this dataset and county-level cadastral data in northwest Montana and compares those results to the SILVIS data. Additionally, each dataset was buffered 1½-miles and compared for spatial coincidence to measure the potential of the LandScan USA data to predict the location of the WUI. The findings reveal that the LandScan USA data do not adequately predict the location of structures for use in wildfire management and planning. However, this research does indicate that further research into LandScan USA’s ability to demarcate the WUI is justified

Simulación paramétrica paralela. Aplicación a modelos de predicción de inundaciones.

El modelado y la simulación de inundaciones provocadas por el desborde de ríos brinda sistemas computacionales para el estudio y la predicción de estos fenómenos naturales, con el objetivo de pronosticar su comportamiento. Estos sistemas necesitan tomar gran cantidad de datos de entrada para aumentar su precisión, como también deben generar múltiples escenarios para cubrir todas las situaciones de riesgo. Por esto, son de cómputo intensivo y pueden tomar días de procesamiento hasta lograr resultados. A este problema se le suma la falta de certeza en los valores de los datos de entrada del proceso. Mediante la programación paralela y los avances en cómputo de alto rendimiento en clusters de computadoras, se pretende atenuar el problema de la incertidumbre de los datos de entrada y optimizar el proceso de predicción mediante la simulación de múltiples escenarios. Con este trabajo se pretende desarrollar una metodología para optimizar la predicción de inundaciones provocadas por el desborde de ríos, en principio de llanuras o planicies, y en particular en la Cuenca del Río Salado o en el Paraná Medio.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI