Giuseppe A. Trunfio

Predicting Wildfire Spreading Through a Hexagonal Cellular Automata Model

As it is well known forest fires can present serious risk to people and can have enormous environmental impact. Therefore researchers and land managers are increasingly interested in effective tools for use in scientific analyses, management and fighting operations. On the other hand forest fires are complex phenomena that need an interdisciplinary approach. In this paper the paradigm of Cellular Automata was applied and a model was projected to simulate the evolution of forest fires. The adopted method involves the definition of local rules, mainly based on fire spread relationships originally developed by Rothermel in 1972, from which the global behaviour of the system can emerge. The preliminary results show that the model could be applied for forest fire prevention, the production of risk scenarios and the evaluation of the forest fire environmental impact.

Mixed formulation and locking in path-following nonlinear analysis

Abstract The arc-length Riks strategy has rapidly become a standard tool for path-following analysis of nonlinear structures due to its theoretical ability to surpass limit points. The aim of this paper is to show that the failures in convergence that are occasionally experienced are not related to proper defects of the algorithm but come from a subtle ‘locking’ effect intrinsic to the nonlinear nature of the problem. As a consequence, its sanitization has to be pursued within a reformulation of the structural model. The use of a mixed (stress-displacement) variant of the algorithm, in particular, appears very promising in this respect. The topic is discussed with reference to the analysis of nonlinear frames using a mixed version of the nonlinear beam model discussed in [39]. It is shown that, with no extra computational cost and only a minor modification in coding with respect to a purely compatible formulation, it is possible to achieve a noticeable improvement in convergence and a real gain in both computational time and overall robustness of the algorithm.

A New Algorithm for Simulating Wildfire Spread through Cellular Automata

Cell-based methods for simulating wildfires can be computationally more efficient than techniques based on the fire perimeter expansion. In spite of this, their success has been limited by the distortions that plague the simulated shapes. This article presents a novel algorithm for wildfire simulation through Cellular Automata (CA), which is able to effectively mitigate the problem of distorted fire shapes. Such a result is obtained allowing spread directions that are not constrained to the few angles imposed by the lattice of cells and the neighborhood size. The characteristics of the proposed algorithm are empirically investigated under homogeneous conditions through some comparisons with the outcomes of a typical CA-based simulator. Also, using two significant heterogeneous landscapes, a comparison with the vector-based simulator FARSITE is discussed. According to the results of this study, the proposed approach performs significantly better, in terms of accuracy, than the CA taken as reference. In addition, at a far less computational cost, it provides burned regions that are equivalent, for practical purposes, to those given by FARSITE.

SCIARA γ2: An improved cellular automata model for lava flows and applications to the 2002 Etnean crisis

Abstract Cellular automata are widely utilized for modelling and simulating complex dynamical systems whose evolution depends on the local interactions of their constituent parts. Simulation by Cellular Interactive Automata of the Rheology of Aetnean lava flows (SCIARA) is a Cellular Automata model for simulating lava flows; its release γ 2 introduces innovations to the empirical method for modelling macroscopic phenomena that was utilized in the previous releases. The lava flows are described as “blocks”, individuated by their barycentre co-ordinates and velocities. This approach is different from the previous releases of SCIARA and from cellular automata derived models for fluid-dynamical phenomena such as lattice-gas and lattice-Boltzmann models. Block specifications permit to obtain a more physical description of the phenomenon and a more accurate control of its development. SCIARA γ 2 was applied to the 2002 Etnean lava flows with satisfying results, obtaining better simulations in comparison with the previous releases.

An Evolutionary approach for modelling lava flows through cellular automata

A Master-Slave Genetic Algorithm is applied to evolve a two-dimensional Cellular Automata model for lava flow simulation. The 2002 Etnean Linguaglossa case study is considered for model calibration. A quantitative measure for the evaluation of the simulations result with respect to the real event is defined and employed as fitness function.

Accelerating wildfire susceptibility mapping through GPGPU

In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape.

The latest release of the lava flows simulation model SCIARA: first application to Mt Etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface

Abstract This paper presents the latest developments of the deterministic Macroscopic Cellular Automata model SCIARA for simulating lava flows. A Bingham-like rheology has been introduced for the first time as part of the Minimization Algorithm of the Differences, which is applied for computing lava outflows from the generic cell towards its neighbours. The hexagonal cellular space adopted in the previous releases of the model for mitigating the anisotropic flow direction problem has been replaced by a–Moore neighbourhood–square one, nevertheless by producing an even better solution for the anisotropic effect. Furthermore, many improvements have been introduced concerning the important modelling aspect of lava cooling. The model has been tested with encouraging results by considering both a real case of study, the 2006 lava flows at Mt Etna (Italy), and an ideal surface, namely a 5°inclined plane, in order to evaluate the magnitude of the anisotropic effect. As a matter of fact, notwithstanding a preliminary calibration, the model demonstrated to be more accurate than its predecessors, providing the best results ever obtained on the simulation of the considered real case of study. Eventually, experiments performed on the inclined plane have pointed out how this release of SCIARA does not present the typical anisotropic problem of deterministic Cellular Automata models for fluids on ideal surfaces.

Cellular automata simulation of urban dynamics through GPGPU

In recent years, urban models based on Cellular Automata (CA) are becoming increasingly sophisticated and are being applied to real-world problems covering large geographical areas. As a result, they often require extended computing times. However, in spite of the improved availability of parallel computing facilities, the applications in the field of urban and regional dynamics are almost always based on sequential algorithms. This paper makes a contribution toward a wider use in the field of geosimulation of high performance computing techniques based on General-Purpose computing on Graphics Processing Units (GPGPU). In particular, we investigate the parallel speedup achieved by applying GPGPU to a popular constrained urban CA model. The major contribution of this work is in the specific modeling we propose to achieve significant gains in computing time, while maintaining the most relevant features of the traditional sequential model.

Cellular Automata and GPGPU: An Application to Lava Flow Modeling

This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture CUDA interface developed by NVIDIA and carried out on Graphical Processing Units GPU. GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

A general-purpose geosimulation infrastructure for spatial decision support

In this paper we present the general-purpose simulation infrastructure MAGI, with features and computational strategies particularly relevant for strongly geo-spatially oriented simulations. Its main characteristics are (1) a comprehensive approach to geosimulation modelling, with a flexible underlying meta-model formally generalising a variety of types of models, both from the cellular automata and from the agent-based family of models, (2) tight interoperability between GIS and the modelling environment, (3) computationally efficiency and (4) user-friendliness. Both raster and vector representation of simulated entities are allowed and managed with efficiency, which is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents. We furthermore present three test-case applications to discuss its efficiency, to present a standard operational modelling workflow within the simulation environment and to briefly illustrate its look-and-feel.