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Natan Micheletti Loris Foresti Sylvain Robert Michael Leuenberger Andrea Pedrazzini Michel Jaboyedoff Mikhail Kanevski 《Mathematical Geosciences》2014,46(1):33-57
This paper explores the use of adaptive support vector machines, random forests and AdaBoost for landslide susceptibility mapping in three separated regions of Canton Vaud, Switzerland, based on a set of geological, hydrological and morphological features. The feature selection properties of the three algorithms are studied to analyze the relevance of features in controlling the spatial distribution of landslides. The elimination of irrelevant features gives simpler, lower dimensional models while keeping the classification performance high. An object-based sampling procedure is considered to reduce the spatial autocorrelation of data and to estimate more reliably generalization skills when applying the model to predict the occurrence of new unknown landslides. The accuracy of the models, the relevance of features and the quality of landslide susceptibility maps were found to be high in the regions characterized by shallow landslides and low in the ones with deep-seated landslides. Despite providing similar skill, random forests and AdaBoost were found to be more efficient in performing feature selection than adaptive support vector machines. The results of this study reveal the strengths of the classification algorithms, but evidence: (1) the need for relying on more than one method for the identification of relevant variables; (2) the weakness of the adaptive scaling algorithm when used with landslide data; and (3) the lack of additional features which characterize the spatial distribution of deep-seated landslides. 相似文献
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Alexei Pozdnoukhov Mikhail Kanevski 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(5):647-660
The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining
and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming
at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data
model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments
in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector
regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial
scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically
from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the
data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations
at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the
possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early
warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application
to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident. 相似文献
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Data-driven topo-climatic mapping with machine learning methods 总被引：1，自引：1，他引：0
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. 相似文献
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A Special Issue on Data Science for Geosciences 总被引：1，自引：0，他引：1
Mathematical Geosciences - 相似文献
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V. Demyanov S. Soltani M. Kanevski S. Canu M. Maignan E. Savelieva V. Timonin V. Pisarenko 《Stochastic Environmental Research and Risk Assessment (SERRA)》2001,15(1):18-32
This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical
prediction (kriging) is proposed. The method – wavelet analysis residual kriging (WARK) – is developed in order to assess
the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have
very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals
focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present
work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network
residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear
trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing
global statistical characteristics of the distribution and spatial correlation structure. 相似文献
6.
Luciano Telesca Michele Lovallo Jean Golay Mikhail Kanevski 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(1):77-90
In this paper, we propose to compare different declustering methods on the basis of the time-correlation and the space-clustering of the residual earthquake catalog after the declustering techniques have been applied. To this aim, we applied two point process clustering measures, the Allan Factor and the Morisita Index, for the identification and quantification of temporal correlation and spatial clustering in point processes, respectively. We used our joint space–time approach to study the earthquake space–time point processes of southern California and Switzerland with surrounding area, declustered by using the method of Gardner and Knopoff (with Grünthal and Uhmhammer window) and that of Reasenberg (with different setting parameters). Our results show that the residual declustered catalog is still characterized by time-correlated structures at long timescales; however, the cutoff timescale that is the lowest timescale above which the time-correlation is visible is higher with the Reasenberg method while is smaller with the Gardner and Knopoff method with Grünthal window. The space-clustering analysis performed by means of the Morisita Index suggests that the declustering technique effectively reduces the spatial clustering of the seismicity of Switzerland, but does not change the spatial properties of the residual seismic catalogue of the southern California. 相似文献
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Loris Foresti Devis Tuia Mikhail Kanevski Alexei Pozdnoukhov 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(1):51-66
This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool.
The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a
given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear
robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL
as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains.
Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain
features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without
degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training
data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit
a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region. 相似文献
8.
M. V. Popov V. A. Soglasnov V. I. Kondrat’ev A. V. Bilous S. V. Sazankov A. I. Smirnov B. Z. Kanevskiĭ V. V. Oreshko Yu. P. Ilyasov 《Astronomy Reports》2008,52(11):900-909
We present the results of long-term, three-frequency monitoring of giant pulses from the Crab pulsar on the 64-m radio telescope in Kalyazin. The total monitoring time was 160 hours. The signal power was recorded simultaneously at 600, 1650, and 4850 MHz via direct sampling of the received signals in the total receiver bandwidth without any compensation for interstellar dispersion. In total, 1117 and 352 giant pulses were detected at 600 and 4850 MHz, respectively. The frequency band centered at 1650 MHz was contaminated by interference, and was used only to identify events found in other frequency bands. The cumulative energy distribution of the giant pulses follows a power law at 600 and 4850 MHz up to the highest energies. A deep modulation in the radio spectra of individual giant pulses was observed on both large (Δv/v ≈ 0.5) and small (Δv/v ≈ (2?4) × 10?3) frequency scales. The simultaneous appearance of giant pulses at the interpulse longitudes at high (4850 MHz) and low (1650 and/or 600 MHz) frequencies testifies to their common origin, in spite of the observed differences in other parameters. 相似文献
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