Neural Network-Based Spatial Modeling of Natural Phenomena and Events

Neural Network-Based Spatial Modeling of Natural Phenomena and Events

Andreas Barth (Beak Consultants GmbH, Germany), Andreas Knobloch (Beak Consultants GmbH, Germany), Silke Noack (Beak Consultants GmbH, Germany) and Frank Schmidt (Beak Consultants GmbH, Germany)
DOI: 10.4018/978-1-4666-6098-4.ch008


Artificial Neural Networks (ANN) are used for statistical modeling of spatial events in geosciences. The advantage of this method is the ability of neural networks to represent complex interrelations and to be “able to learn” from known (spatial) events. The software advangeo® was developed to enable GIS users to apply neural network methods on raster geodata. The statistic modeling results can be developed and displayed in a user-friendly way within the Esri ArcGIS environment. The complete workflow is documented by the software. This chapter presents five case studies to illustrate the current possibilities and limitations of spatial predictions with the use of artificial neural networks, which describe influencing factors and the selection of known events of the phenomenon to be modeled. These applications include: (1) the prognosis of soil erosion patterns, (2) the country-wide prediction of mineral resources, (3) the vulnerability analysis for forest pests, (4) the spatial distribution of bird species, and (5) the spatial prediction of manganese nodules on the sea bottom.
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Natural phenomena and events are usually caused by a complex of interacting factors. An exact mathematical formulation of a geo-scientific task, however, with equations describing the dependence of a phenomenon on several main influencing factors, is rarely feasible. Typically, a model only refers to some aspects of the phenomenon in question. Due to the lack of data and knowledge of details of many geo-processes, mathematical models cannot be successfully defined and applied with reasonable certainty. This paper explores artificial neural networks (ANN) (Hassoun 1995, Kasabov 1996, Haykins 1998, Bishop 2008) as a means to provide a reliable tool to analyze causal relationships and to make the knowledge available for predictive processes. This approach differs from traditional methods in that viable results may be obtained with reasonable efforts invested in data processing, model design and computational time.

Results for five different case studies are discussed in this paper, following an introduction on the theoretical background of the neural network approach. The outcomes are predictive maps, which illustrate the favorabilities of occurrence for a given phenomenon. This forms an important basis for the planning of further (economic) activities. The case studies deal with various tasks in applied earth sciences and demonstrate the applicability of ANN/GIS approach. In particular, the following objectives applied to the case studies presented here are:

  • 1.

    Spatial prediction of soil erosion channels to localize damages (on-site/off-site), and to model effects of mitigation measures;

  • 2.

    Spatial prediction of the most promising locations for mineral exploration within a country, based on available geological data;

  • 3.

    Spatial prediction of the most vulnerable forest stands in case of spreading bark beetle infections;

  • 4.

    Prediction of spatial distribution of bird species according to a landuse/habitat map;

  • 5.

    Spatial prediction of poly-metallic (manganese) nodules on the sea bottom.

Although the value of the application of neural network technologies in GIS environments was recognized in the past, actual application of this remained a challenge for standard users due to the lack of user-friendly tools (see separation of ANN and GIS in Lamothe (2009) and Brown et al. 2003). Thus, the overall goal of the case studies was to study the quality of model outputs and the general applicability of the ANN/GIS approach included in the software advangeo®.


Rationale And Objectives Of The Study: Selection Of An Appropriate Modelling Method For Spatial Events

The location of a spatial event both naturally or human induced is determined by a complex network of influential causes and subsequent effects. Hence, the relationships between the parameters are usually characterized by qualitative descriptors rather than quantifiable means if they can be described at all in a reasonable amount of time. In many cases, data rather than rules exist (Brown et al. 2003, Maier & Dandy 2000). This hampers the mathematical modeling and spatial prediction of natural phenomena. In general, spatial pattern of events can be modeled by two different approaches:

Firstly by conducting detailed studies of physical, chemical and other relations to establish an accurate quantitative model of the processes with mathematical-analytical methods, for example when using finite elements to model slope stability. With this approach, equations are used to parameterize and model natural processes. Equation calibration is usually accomplished by adjusting “constants”, based on the comparison of modeled results and measured data.

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