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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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Foresti G.L. Murino V. Regazzoni C.S. Trucco A. 《Oceanic Engineering, IEEE Journal of》1997,22(1):57-65
This paper describes a voting-based approach for the fast automatic recognition of man-made objects and related attitude estimation in underwater acoustic images generated by forward-looking sonars or acoustic cameras. In general, the continuous analysis of sequences of images is a very heavy task for human operators and this is due to the poor quality of acoustic images. Hence, algorithms able to recognize an object on the basis of a priori knowledge of the model and to estimate its attitude with reference to a global coordinate system are very useful to facilitate underwater operations like object manipulation or vehicle navigation. The proposed method is capable of recognizing objects and estimating their two-dimensional attitude by using information coming from boundary segments and their angular relations. It is based on a simple voting approach directly applied to the edge discontinuities of underwater acoustic images, whose quality is usually affected by some undesired effects such as object blurring, speckle noise, and geometrical distortions degrading the edge detection. The voting approach is robust, with respect to these effects, so that good results are obtained even with images of very poor quality. The sequences of simulated and real acoustic images are presented in order to test the validity of the proposed method in terms of average estimation error and computational load 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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In this paper, a hierarchical system, in which each level is composed by a neural-based classifier, is proposed to recognize objects in underwater images. The system has been designed to help an autonomous underwater vehicle in sea-bottom survey operations, like pipeline inspections. The input image is divided into square regions (macro-pixels) and a neural tree is used to classify each region into different object classes (pipeline, sea-bottom, or anodes). Each macro-pixel is then analyzed according to some geometric and environment constraints: macro-pixels with doubt classification are divided into four parts and re-classified. The process is iterated until the desired accuracy is reached. Experimental results, which have been performed on a large set of real underwater images acquired in different sea environments, demonstrate the robustness and the accuracy of the proposed system 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
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