A Language-centered Approach to support environmental modeling with Cellular Automata

Die Anwendung von Methodiken und Technologien aus dem Bereich der Softwaretechnik auf den Bereich der Umweltmodellierung ist eine gemeinhin akzeptierte Vorgehensweise. Im Rahmen der "modellgetriebenen Entwicklung"(MDE, model-driven engineering) werden Technologien entwickelt, die darauf abzielen, Softwaresysteme vorwiegend auf Basis von im Vergleich zu Programmquelltexten relativ abstrakten Modellen zu entwickeln. Ein wesentlicher Bestandteil von MDE sind Techniken zur effizienten Entwicklung von "domänenspezifischen Sprachen"( DSL, domain-specific language), die auf Sprachmetamodellen beruhen. Die vorliegende Arbeit zeigt, wie modellgetriebene Entwicklung, und insbesondere die metamodellbasierte Beschreibung von DSLs, darüber hinaus Aspekte der Pragmatik unterstützen kann, deren Relevanz im erkenntnistheoretischen und kognitiven Hintergrund wissenschaftlichen Forschens begründet wird. Hierzu wird vor dem Hintergrund der Erkenntnisse des "modellbasierten Forschens"(model-based science und model-based reasoning) gezeigt, wie insbesondere durch Metamodelle beschriebene DSLs Möglichkeiten bieten, entsprechende pragmatische Aspekte besonders zu berücksichtigen, indem sie als Werkzeug zur Erkenntnisgewinnung aufgefasst werden. Dies ist v.a. im Kontext großer Unsicherheiten, wie sie für weite Teile der Umweltmodellierung charakterisierend sind, von grundsätzlicher Bedeutung. Die Formulierung eines sprachzentrierten Ansatzes (LCA, language-centered approach) für die Werkzeugunterstützung konkretisiert die genannten Aspekte und bildet die Basis für eine beispielhafte Implementierung eines Werkzeuges mit einer DSL für die Beschreibung von Zellulären Automaten (ZA) für die Umweltmodellierung. Anwendungsfälle belegen die Verwendbarkeit von ECAL und der entsprechenden metamodellbasierten Werkzeugimplementierung.The application of methods and technologies of software engineering to environmental modeling and simulation (EMS) is common, since both areas share basic issues of software development and digital simulation. Recent developments within the context of "Model-driven Engineering" (MDE) aim at supporting the development of software systems at the base of relatively abstract models as opposed to programming language code. A basic ingredient of MDE is the development of methods that allow the efficient development of "domain-specific languages" (DSL), in particular at the base of language metamodels. This thesis shows how MDE and language metamodeling in particular, may support pragmatic aspects that reflect epistemic and cognitive aspects of scientific investigations. For this, DSLs and language metamodeling in particular are set into the context of "model-based science" and "model-based reasoning". It is shown that the specific properties of metamodel-based DSLs may be used to support those properties, in particular transparency, which are of particular relevance against the background of uncertainty, that is a characterizing property of EMS. The findings are the base for the formulation of an corresponding specific metamodel- based approach for the provision of modeling tools for EMS (Language-centered Approach, LCA), which has been implemented (modeling tool ECA-EMS), including a new DSL for CA modeling for EMS (ECAL). At the base of this implementation, the applicability of this approach is shown

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Application of Model-driven engineering to multi-agent systems: a language to model behaviors of reactive agents

Many users of multi-agent systems (MAS) are very commonly disinclined to model and simulate using current MAS platforms. More specifically, modeling the dynamics of a system (in particular the agents' behaviors) is very often a challenge to MAS users. This issue is more often observed in the domain of socio-ecological systems (SES), because SES domain experts are rarely programmers. Indeed, the majority of MAS platforms were not conceived taking into consideration domain-experts who are non-programmers. Most current MAS tools are not dedicated to SES, or nor do they possess an easily understandable formalism to represent the behaviors of agents. Moreover, because it is platform-dependent, a model realized in a given MAS platform cannot be properly used on another platform due to incompatibility between MAS platforms. To overcome these limitations, we propose a domain-specific language (DSL) to describe the behaviors of reactive agents, regardless of the MAS platform used for simulation. To achieve this result, we used model-driven engineering (MDE), an approach that provides tools to develop DSLs from a meta-model (abstract syntax), textual editors with syntax highlighting (for the concrete syntax) and code generation capabilities (for source-code generation of a model). As a result, we implemented a language and a textual editor that allow SES domain experts to describe behaviors in three different ways that are close to their natural expression: as equations when they are familiar with these, as a sequence of activities close to natural language or as an activity diagram to represent decisions and a sequence of behaviors using a graphic formalism. To demonstrate interoperability, we also developed code generators targeting two different MAS platforms (Cormas and Netlogo). We tested the code generators by implementing two SES models with the developed DSL. The generated code was targeted to both MAS platforms (Cormas and Netlogo), and successfully simulated in one of them. We conclude that the MDE approach provides adequate tools to develop DSL and code generators to facilitate MAS modeling and simulation by non-programmers. Concerning the DSL developed, although the behavioral aspect of MAS simulation is part of the complexity of modeling in MAS, there are still other essential aspects of model and simulation of MAS that are yet to be explored, such as model initialization and points of view on the model simulated worl

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Some results on a set of data driven stochastic wildfire models

Across the globe, the frequency and size of wildfire events are increasing. Research focused on minimizing wildfire is critically needed to mitigate impending humanitarian and environmental crises. Real-time wildfire response is dependent on timely and accurate prediction of dynamic wildfire fronts. Current models used to inform decisions made by the U.S. Forest Service, such as Farsite, FlamMap and Behave do not incorporate modern remotely sensed wildfire records and are typically deterministic, making uncertainty calculations difficult. In this research, we tested two methods that combine artificial intelligence with remote sensing data. First, a stochastic cellular automata that learns algebraic expressions was fit to the spread of synthetic wildfire through symbolic regression. The validity of the genetic program was tested against synthetic spreading behavior driven by a balanced logistic model. We also tested a deep learning approach to wildfire fire perimeter prediction. Trained on a time-series of geolocated fire perimeters, atmospheric conditions, and satellite images, a deep convolutional neural network forecasts the evolution of the fire front in 24-hour intervals. The approach yielded several relevant high-level abstractions of input data such as NDVI vegetation indexes and produced promising initial results. These novel data-driven methods leveraged abundant and accessible remote sensing data, which are largely unused in industry level wildfire modeling. This work represents a step forward in wildfire modeling through a curated aggregation of satellite image spectral layers, historic wildfire perimeter maps, LiDAR, atmospheric conditions, and two novel simulation models. The results can be used to train and validate future wildfire models, and offer viable alternatives to current benchmark physics-based models used in industry

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned