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Data-driven topo-climatic mapping with machine learning methods

Authors
Journal
Natural Hazards
0921-030X
Publisher
Springer-Verlag
Publication Date
Volume
50
Issue
3
Identifiers
DOI: 10.1007/s11069-008-9339-y
Keywords
  • Machine Learning
  • Support Vector Machine
  • Topo-Climatic Mapping
  • Feature
  • Selection
  • Environmental Modelling
  • Downscaling
  • Decision Support Systems
  • Natural Hazards
Disciplines
  • Communication
  • Computer Science
  • Earth Science
  • Ecology
  • Geography

Abstract

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

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