In a series of three studies, we assess modern debris-flow hazards in Arizona from extreme precipitation events and following wildfires. In the first study, we use a combination of surficial geologic mapping, ¹⁰Be exposure age dating and modeling to assess prehistoric to modern debris-flow deposits on two alluvial fans in order to place debris-flow hazards in the context of both the modern environment and the last major period of climate change. Late Pleistocene to early Holocene debris flows were larger and likely initiated by larger landslides or other mass movement failures, unlike recent debris flows that typically initiate from shallow (~1 m) failures and scour channels, thus limiting total volumes. In the second study we assess the predictive strengths of existing post wildfire debris-flow probability and volume models for use in Arizona's varied physiographic regions, and define a new rainfall threshold valid for Arizona. We show that all of the models have adequate predictive strength throughout most of the state, and that the debris-flow volume model over-predicts in all of our study areas. Our analysis shows that the choice of a model for a hazard assessment depends strongly on location. The objectively defined rainfall intensity-duration thresholds of I₁₀ and I₁₅ (52 and 42 mm h⁻¹, respectively) have the strongest predictive strengths, although all five of the threshold models performed well. In the third study, we explore various basin physiographic and soil burn severity factors to identify patterns and criteria that can be used to discriminate between potential non-debris-flow (nD) and debris-flow (D) producing basins. Findings from this study show that a metric of percent basins area with high soil burn severity on slopes ≥30 degrees provides a stronger discrimination between nD and D basins than do basin metrics, such as mean basin gradient or relief. Mean basin elevation was also found to discriminate nD from D basins and is likely a proxy for forest type and density, which relates to soil thickness, root density and the magnitude of post-disturbance erosion. Finally, we found that post-fire channel heads formed at essentially the same slope range (~30-40 degrees) as saturation-induced hill slope failures.

The purpose of this study is to assess the engineering geological and geotechnical characteristics and to perform seismic hazard studies of the Upper Pliocene to Quaternary (Plio-Quaternary) deposits located towards the north of Ankara through surface wave testing methods. Based on a general engineering geological and seismic site characterization studies, site classification systems are assigned in seismic hazard assessments. The objective of the research is to determine the regional and local seismic soil conditions (i.e., shear wave velocities, soil predominant periods and soil amplification factors) and to characterize the soil profile of the sites in this region by the help of surface geophysical methods. These studies have been supported by engineering geological and geotechnical field studies carried out prior to and during this study. By integrating these studies, local soil conditions and dynamic soil characteristics for the study area have been assessed by detailed soil characterization in the region. As a result, seismic hazard assessments have been performed for &Ccedil / ubuk and its close vicinity with the aid of Geographical Information Systems (GIS) through establishing seismic characterization and local soil conditions of the area.

Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. / Obwohl Naturgefahren in ihren Ursachen, Erscheinungen und Auswirkungen grundlegend verschieden sind, teilen sie doch viele Gemeinsamkeiten und Herausforderungen, wenn es um ihre Modellierung geht. Fehlendes Wissen über die zugrunde liegenden Kräfte und deren komplexes Zusammenwirken erschweren die Wahl einer geeigneten Modellstruktur. Hinzu kommen ungenaue und unvollständige Beobachtungsdaten sowie dem Naturereignis innewohnende Zufallsprozesse. All diese verschiedenen, miteinander interagierende Aspekte von Unsicherheit erfordern eine sorgfältige Betrachtung, um fehlerhafte und verharmlosende Einschätzungen von Naturgefahren zu vermeiden. Dennoch sind deterministische Vorgehensweisen in Gefährdungsanalysen weit verbreitet. Bayessche Netze betrachten die Probleme aus wahrscheinlichkeitstheoretischer Sicht und bieten somit eine sinnvolle Alternative zu deterministischen Verfahren. Alle vom Zufall beeinflussten Größen werden hierbei als Zufallsvariablen angesehen. Die gemeinsame Wahrscheinlichkeitsverteilung aller Variablen beschreibt das Zusammenwirken der verschiedenen Einflussgrößen und die zugehörige Unsicherheit/Zufälligkeit. Die Abhängigkeitsstrukturen der Variablen können durch eine grafische Darstellung abgebildet werden. Die Variablen werden dabei als Knoten in einem Graphen/Netzwerk dargestellt und die (Un-)Abhängigkeiten zwischen den Variablen als (fehlende) Verbindungen zwischen diesen Knoten. Die dargestellten Unabhängigkeiten veranschaulichen, wie sich die gemeinsame Wahrscheinlichkeitsverteilung in ein Produkt lokaler, bedingter Wahrscheinlichkeitsverteilungen zerlegen lässt. Im Verlauf dieser Arbeit werden verschiedene Naturgefahren (Erdbeben, Hochwasser und Bergstürze) betrachtet und mit Bayesschen Netzen modelliert. Dazu wird jeweils nach der Netzwerkstruktur gesucht, welche die Abhängigkeiten der Variablen am besten beschreibt. Außerdem werden die Parameter der lokalen, bedingten Wahrscheinlichkeitsverteilungen geschätzt, um das Bayessche Netz und dessen zugehörige gemeinsame Wahrscheinlichkeitsverteilung vollständig zu bestimmen. Die Definition des Bayesschen Netzes kann auf Grundlage von Expertenwissen erfolgen oder - so wie in dieser Arbeit - anhand von Beobachtungsdaten des zu untersuchenden Naturereignisses. Die hier verwendeten Methoden wählen Netzwerkstruktur und Parameter so, dass die daraus resultierende Wahrscheinlichkeitsverteilung den beobachteten Daten eine möglichst große Wahrscheinlichkeit zuspricht. Da dieses Vorgehen keine Expertenwissen voraussetzt, ist es universell in verschiedenen Gebieten der Gefährdungsanalyse einsetzbar. Trotz umfangreicher Forschung zu diesem Thema ist das Bestimmen von Bayesschen Netzen basierend auf Beobachtungsdaten nicht ohne Schwierigkeiten. Typische Herausforderungen stellen die Handhabung stetiger Variablen und unvollständiger Datensätze dar. Beide Probleme werden in dieser Arbeit behandelt. Es werden Lösungsansätze entwickelt und in den Anwendungsbeispielen eingesetzt. Eine Kernfrage ist hierbei die Komplexität des Algorithmus. Besonders wenn sowohl stetige Variablen als auch unvollständige Datensätze in Kombination auftreten, sind effizient arbeitende Verfahren gefragt. Die hierzu in dieser Arbeit entwickelten Methoden ermöglichen die Verarbeitung von großen Datensätze mit stetigen Variablen und unvollständigen Beobachtungen und leisten damit einen wichtigen Beitrag für die wahrscheinlichkeitstheoretische Gefährdungsanalyse.

The purpose of this study is to assess the engineering geological and geotechnical characteristics and to perform seismic hazard studies of the Upper Pliocene and Quaternary deposits located towards the west of Ankara. Based on a general engineering geological and seismic characterization of the site, site classification systems are assigned for seismic hazard assessment studies. The objective of the research is to determine the regional and local seismic soil conditions, predominant periods and ground amplifications, and to idealize the soil profile of the sites by the aid of surface geophysical methods. These studies are combined and integrated with the geotechnical database from a variety of in-situ and laboratory studies that are compiled from present and previous studies regarding the project area and then transferred to an analytical environment for creating relevant information for our site. Then, engineering geological and geotechnical seismic characterization along with seismic zoning map preperation is accomplished. Finally, based on a general engineering geological and geotechnical site characterization, site classification systems are assigned to account for site effects in seismic hazard assessments along with the assessment of mitigation and remediation of seismic hazards.