A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices

A multi-scale method to assess pesticide contamination risks in agricultural watersheds

The protection of water is now a major priority for environmental managers, especially around drinkingpumping stations. In view of the new challenges facing water agencies, we developed a method designedto support their public policy decision-making, at a variety of different spatial scales. In this paper, wepresent this new spatial method, using remote sensing and a GIS, designed to determine the contami-nation risk due to agricultural inputs, such as pesticides. The originality of this method lies in the useof a very detailed spatial object, the RSO (Reference Spatial Object), which can be aggregated to manyworking and managing scales. This has been achieved thanks to the pixel size of the remote sensing, witha grid resolution of 30 m × 30 m in our application.The method – called PHYTOPIXAL – is based on a combination of indicators relating to the environmen-tal vulnerability of the surface water environment (slope, soil type and distance to the stream) and theagricultural pressure (land use and practices of the farmers). The combination of these indicators for eachpixel provides the contamination risk. The scoring of variables was implemented according knowledgein literature and of experts.This method is used to target specific agricultural input transfer risks. The risk values are first calculatedfor each pixel. After this initial calculation, the data are then aggregated for decision makers, accordingto the most suitable levels of organisation. These data are based on an average value for the watershedareas.In this paper we detail an application of the method to an area in the hills of Southwest France. Weshow the pesticide contamination risk by in areas with different sized watersheds, ranging from 2 km2to 7000 km2, in which stream water is collected for consumption by humans and animals. The resultswere recently used by the regional water agency to determine the protection zoning for a large pumpingstation. Measures were then proposed to farmers with a view to improving their practices.The method can be extrapolated to different other areas to preserve or restore the surface water

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchment

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure