Spatiotemporal modelling of extreme wildfires and severe thunderstorm environments

Jonathan Koh Boon Han
EPFL, 2022
Thèse EPFL

Résumé

Environmental extreme events can have devastating impacts on society when they interact with vulnerable human and natural systems. Such events can result from natural causes, like phenomena related to the El Ni~no-Southern Oscillation or decadal/multi-decadal climate variations. These causes can follow an increase in human activity, e.g., through land-use changes or anthropogenic climate change, that can influence the frequency, intensity, spatial extent and timing of these events, and spur unprecedented extremes. To accurately understand and quantify the risks associated with these events, it is important to identify trends related to these causes, which may be measured or unmeasured. Fitting models for rare events is inherently difficult because of the paucity of data available. The most destructive extreme events are rarely isolated in space and time, so one must account for their spatial and temporal dependencies. This thesis deals with the parametric modelling of severe thunderstorms and wildfires using models motivated from limiting probabilistic results. The first part of this thesis explores influences on the magnitude and spatial extent of extremes of environments related to severe US thunderstorms. Our results show that the risk from severe thunderstorms in April and May is increasing in parts of the US where it was already high, and that the risk from storms in February increases during La Ni~na years. We also show that these extremes are more localized during spring/summer seasons than in the winter, and find that some of these seasonal differences are more pronounced during El Ni~no years.The second part of the thesis deals with predicting and explaining the spatial extent, frequency, intensity and timing of wildfires using meteorological and land-use covariates. Our first approach uses ideas from extreme-value theory in a machine learning context to give good prediction of the distributional tails of our data. The second approach uses a novel Bayesian hierarchical model designed specifically for extreme wildfires. We show that wildfire risk on the French Mediterranean basin is affected by significant random effects related to land-use and policy changes, and a seasonally-varying fire-weather index.

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https://infoscience.epfl.ch/record/291228?ln=fr

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Jonathan Koh Boon Han
EPFL, 2022
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/291228?ln=fr

À propos de ce résultat

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Concepts associés

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Autism is a neurodevelopmental condition diagnosed by impaired social interaction, abnormal communication and, stereotyped behaviors. While post-mortem and imaging studies have provided good insights into the neurobiological symptomology of autism, animal models can be used to study the neuroanatomical, neurophysiological and molecular mediators in more detail and in a more controlled environment. The valproic acid (VPA) rat model is an environmentally triggered model with strong construct and clinical validity. It is based on VPA teratogenicity in humans, where mothers who are medicated with VPA during early pregnancy show an increased risk for giving birth to an autistic child. In rats, early embryonic exposure, around the time of neural tube closure, leads to autism-like anatomical and behavioral abnormalities in the offspring. Considering the increasing use of the VPA rat model, we present our observations of the general health of Wistar dams treated with a single intraperitoneal injection of 500 or, 600 mg/kg VPA on embryonic day E12.5, as well as their male and female offspring, in comparison to saline-exposed controls. We report increased rates of complete fetal reabsorption after both VPA doses. VPA 500 mg/kg showed no effect on dam body weight during pregnancy or, on litter size. Offspring exposed to VPA 500 mg/kg showed smaller brain mass on postnatal days 1 (P1) and 14 (P14), in addition to abnormal nest seeking behavior at P10 in the olfactory discrimination test, relative to controls. We also report increased rates of physical malformations in the offspring, rare occurrences of chromodacryorrhea and, developmentally similar body mass gain. Further documentation of developmental health may guide sub-grouping of individuals in a way to better predict core symptom severity.

Frontiers Research Foundation2013

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A novel biomechanical approach for animal behaviour recognition using accelerometers

Kamiar Aminian, Pritish Chakravarty

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have been the tool of choice for characterising animal behaviour. Although machine learning models perform reasonably well, they may not rely on meaningful features, nor lend themselves to physical interpretation of the classification rules. This lack of interpretability and control over classification outcomes is of particular concern where different behaviours have different frequency of occurrence and duration, as in most natural systems, and calls for the development of alternative methods. Biomechanical approaches to human activity classification could overcome these shortcomings, yet their full potential remains untapped for animal studies. 2. We propose a general framework for behaviour recognition using accelerometers, and develop a hybrid model where (a) biomechanical features characterise movement dynamics, and (b) a node-based hierarchical classification scheme employs simple machine learning algorithms at each node to find feature-value thresholds separating different behaviours. Using triaxial accelerometer data collected on 10 wild Kalahari meerkats, and annotated video recordings of each individual as groundtruth, this hybrid model was validated in three scenarios: (a) when each behaviour was equally represented (EQDIST), (b) when naturally imbalanced datasets were considered (STRAT) and (c) when data from new individuals were considered (LOIO). 3. A linear-kernel Support Vector Machine at each node of our classification scheme yielded an overall accuracy of >95% for each scenario. Our hybrid approach had a 2.7% better average overall accuracy than top-performing classical machine learning approaches. Further, we showed that not all models with high overall accuracy returned accurate behaviour-specific performance, and good performance during EQDIST did not always generalise to STRAT and LOIO. 4. Our hybrid model took advantage of robust machine learning algorithms for automatically estimating decision boundaries between behavioural classes. This not only achieved high classification performance but also permitted biomechanical interpretation of classification outcomes. The framework presented here provides the flexibility to adapt models to required levels of behavioural resolution, and has the potential to facilitate meaningful model sharing between studies.

2019

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xtreme value analysis is concerned with the modelling of extreme events such as floods and heatwaves, which can have large impacts. Statistical modelling can be useful to better assess risks even if, due to scarcity of measurements, there is inherently very large residual uncertainty in any analysis. Driven by the increase in environmental databases, spatial modelling of extremes has expanded rapidly in the last decade. This thesis presents contributions to such analysis. The first chapter is about likelihood-based inference in the univariate setting and investigates the use of bias-correction and higher-order asymptotic methods for extremes, highlighting through examples and illustrations the unique challenge posed by data scarcity. We focus on parametric modelling of extreme values, which relies on limiting distributional results and for which, as a result, uncertainty quantification is complicated. We find that, in certain cases, small-sample asymptotic methods can give improved inference by reducing the error rate of confidence intervals. Two data illustrations, linked to assessment of the frequency of extreme rainfall episodes in Venezuela and the analysis of survival of supercentenarians, illustrate the methods developed. In the second chapter, we review the major methods for the analysis of spatial extremes models. We highlight the similarities and provide a thorough literature review along with novel simulation algorithms. The methods described therein are made available through a statistical software package. The last chapter focuses on estimation for a Bayesian hierarchical model derived from a multivariate generalized Pareto process. We review approaches for the estimation of censored components in models derived from (log)-elliptical distributions, paying particular attention to the estimation of a high-dimensional Gaussian distribution function via Monte Carlo methods. The impacts of model misspecification and of censoring are explored through extensive simulations and we conclude with a case study of rainfall extremes in Eastern Switzerland.

EPFL2019

Chargement

A novel biomechanical approach for animal behaviour recognition using accelerometers

Kamiar Aminian, Pritish Chakravarty

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have been the tool of choice for characterising animal behaviour. Although machine learning models perform reasonably well, they may not rely on meaningful features, nor lend themselves to physical interpretation of the classification rules. This lack of interpretability and control over classification outcomes is of particular concern where different behaviours have different frequency of occurrence and duration, as in most natural systems, and calls for the development of alternative methods. Biomechanical approaches to human activity classification could overcome these shortcomings, yet their full potential remains untapped for animal studies. 2. We propose a general framework for behaviour recognition using accelerometers, and develop a hybrid model where (a) biomechanical features characterise movement dynamics, and (b) a node-based hierarchical classification scheme employs simple machine learning algorithms at each node to find feature-value thresholds separating different behaviours. Using triaxial accelerometer data collected on 10 wild Kalahari meerkats, and annotated video recordings of each individual as groundtruth, this hybrid model was validated in three scenarios: (a) when each behaviour was equally represented (EQDIST), (b) when naturally imbalanced datasets were considered (STRAT) and (c) when data from new individuals were considered (LOIO). 3. A linear-kernel Support Vector Machine at each node of our classification scheme yielded an overall accuracy of >95% for each scenario. Our hybrid approach had a 2.7% better average overall accuracy than top-performing classical machine learning approaches. Further, we showed that not all models with high overall accuracy returned accurate behaviour-specific performance, and good performance during EQDIST did not always generalise to STRAT and LOIO. 4. Our hybrid model took advantage of robust machine learning algorithms for automatically estimating decision boundaries between behavioural classes. This not only achieved high classification performance but also permitted biomechanical interpretation of classification outcomes. The framework presented here provides the flexibility to adapt models to required levels of behavioural resolution, and has the potential to facilitate meaningful model sharing between studies.

2019

EPFL2019