Data-driven topo-climatic mapping with machine learning methods

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 nonlinear, 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.

A case‐study of cold‐air pool evolution in hilly terrain using field measurements from COLPEX

“…gritting roads, activating extreme cold weather and/or air-quality action plans), depends on the ability of weather forecast models to accurately predict CAPs. The improved representation of CAPs in weather models is likely to be achieved through (a) the continued development of downscaling techniques (Pozdnoukhov et al, 2009) ; (b) the development of parametrizations; or (c) increased resolution (Vosper et al, 2013;Hughes et al, 2015). In the short term, options (a) and (b) are more practical given that current operational weather forecast models have horizontal resolutions >1 km.…”

Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

“…Each class Z i is associated with a discriminant function f i (x). Several articles are published that compare multiple SL techniques (e.g., Garzón et al 2006;Berrueta et al 2007;Sakiyama et al 2008;PinoMejías et al 2008PinoMejías et al , 2010Pozdnoukhov et al 2009;Zhou et al 2011;González-Rufino et al 2013). Based on these articles and the focus herein on PS classifications, only a brief description of each classification technique will be presented.…”

“…The use of SL algorithms for the development of predictive and descriptive data mining models has become widely accepted in mining and geotechnical applications, promising powerful new tools for practicing engineers (Garzón et al 2006;Berrueta et al 2007;Sakiyama et al 2008;Pino-Mejías et al 2008, 2010Pozdnoukhov et al 2009;Tesfamariam and Liu 2010;Zhou et al 2011Zhou et al , 2012Zhou et al , 2013González-Rufino et al 2013;Liu et al 2013Liu et al , 2014. Numerous approaches for PS prediction have been developed based on different SL techniques during recent decades (Tawadrous and Katsabanis 2007;Zhou et al 2011;Wattimena et al 2013;Ghasemi et al 2014a, b).…”

Data-driven exploration of orographic enhancement of precipitation

“…Features were derived at different spatial scales (degrees of smoothness) by applying convolution kernels with different bandwidths σ. More details about the extraction and the use of these features for meteorological applications can be found in Pozdnoukhov et al (2009) and Foresti et al (2011).…”