Data-driven topo-climatic mapping with machine learning methods

Alexei Pozdnoukhov

doi:10.1007/s11069-008-9339-y

2009-01-16

Alexei Pozdnoukhov *

Loris Foresti

Mikhaïl Kanevski

DOI：10.1007/s11069-008-9339-y

原文链接

原文求助

摘要

En 中文

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 Fohn 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.

Keyword:

Machine learning

Support vector machines

Topo-climatic mapping

Feature selection

Environmental modelling

Downscaling

Decision support systems

Natural hazards