70,954 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studies—often clustered under the collective term of
geocomputation, as they apply to geography—are ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
Estimating the effects of water-induced shallow landslides on soil erosion
Rainfall induced landslides and soil erosion are part of a complex system of
multiple interacting processes, and both are capable of significantly affecting
sediment budgets. These sediment mass movements also have the potential to
significantly impact on a broad network of ecosystems health, functionality and
the services they provide. To support the integrated assessment of these
processes it is necessary to develop reliable modelling architectures. This
paper proposes a semi-quantitative integrated methodology for a robust
assessment of soil erosion rates in data poor regions affected by landslide
activity. It combines heuristic, empirical and probabilistic approaches. This
proposed methodology is based on the geospatial semantic array programming
paradigm and has been implemented on a catchment scale methodology using
Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The
integrated data-transformation model relies on a modular architecture, where
the information flow among modules is constrained by semantic checks. In order
to improve computational reproducibility, the geospatial data transformations
implemented in ESRI ArcGis are made available in the free software GRASS GIS.
The proposed modelling architecture is flexible enough for future
transdisciplinary scenario analysis to be more easily designed. In particular,
the architecture might contribute as a novel component to simplify future
integrated analyses of the potential impact of wildfires or vegetation types
and distributions, on sediment transport from water induced landslides and
erosion.Comment: 14 pages, 4 figures, 1 table, published in IEEE Earthzine 2014 Vol. 7
Issue 2, 910137+ 2nd quarter theme. Geospatial Semantic Array Programming.
Available: http://www.earthzine.org/?p=91013
An Architecture for Integrated Intelligence in Urban Management using Cloud Computing
With the emergence of new methodologies and technologies it has now become
possible to manage large amounts of environmental sensing data and apply new
integrated computing models to acquire information intelligence. This paper
advocates the application of cloud capacity to support the information,
communication and decision making needs of a wide variety of stakeholders in
the complex business of the management of urban and regional development. The
complexity lies in the interactions and impacts embodied in the concept of the
urban-ecosystem at various governance levels. This highlights the need for more
effective integrated environmental management systems. This paper offers a
user-orientated approach based on requirements for an effective management of
the urban-ecosystem and the potential contributions that can be supported by
the cloud computing community. Furthermore, the commonality of the influence of
the drivers of change at the urban level offers the opportunity for the cloud
computing community to develop generic solutions that can serve the needs of
hundreds of cities from Europe and indeed globally.Comment: 6 pages, 3 figure
MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS
The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,
Modeling structural change in spatial system dynamics: A Daisyworld example
System dynamics (SD) is an effective approach for helping reveal the temporal
behavior of complex systems. Although there have been recent developments in
expanding SD to include systems' spatial dependencies, most applications have
been restricted to the simulation of diffusion processes; this is especially
true for models on structural change (e.g. LULC modeling). To address this
shortcoming, a Python program is proposed to tightly couple SD software to a
Geographic Information System (GIS). The approach provides the required
capacities for handling bidirectional and synchronized interactions of
operations between SD and GIS. In order to illustrate the concept and the
techniques proposed for simulating structural changes, a fictitious environment
called Daisyworld has been recreated in a spatial system dynamics (SSD)
environment. The comparison of spatial and non-spatial simulations emphasizes
the importance of considering spatio-temporal feedbacks. Finally, practical
applications of structural change models in agriculture and disaster management
are proposed
An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)
Land use change has important consequences for biodiversity and the
sustainability of ecosystem services, as well as for global
environmental change. Spatially explicit land use change models
improve our understanding of the processes driving change and make
predictions about the quantity and location of future and past
change. Here we present the lulccR package, an object-oriented
framework for land use change modelling written in the R programming
language. The contribution of the work is to resolve the following
limitations associated with the current land use change modelling
paradigm: (1) the source code for model implementations is
frequently unavailable, severely compromising the reproducibility of
scientific results and making it impossible for members of the
community to improve or adapt models for their own purposes; (2)
ensemble experiments to capture model structural uncertainty are
difficult because of fundamental differences between implementations
of different models; (3) different aspects of the modelling
procedure must be performed in different environments because
existing applications usually only perform the spatial allocation of
change. The package includes a stochastic ordered allocation
procedure as well as an implementation of the widely used CLUE-S
algorithm. We demonstrate its functionality by simulating land use
change at the Plum Island Ecosystems site, using a dataset included
with the package. It is envisaged that lulccR will enable future
model development and comparison within an open environment
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