Soft-computing techniques in soil hydrological parameters modelling

1 copia .pdf (15 Pags., with Figs. y Tabls.) de la presentación orginal de los autores en el Congreso Internacional.The idea of the soft-computing Soft-computing models: * based on the data learning, * does not provide analytical solution of the problem,solutions are by by de nition inexact and approximate, * allow for modelling of the properties or behaviour of the complex systems without deep insight, * gives speci c solution for currently modelled phenomenon. Classical models: * based on full physical-mathematical modelling, * described by some class of exact mathematical equations, * often expensive in the sense resources utilised for solving the problem (computing time), * universal for the given phenomenon described.Peer reviewe

European HYdropedological Data Inventory (EU-HYDI)

There is a common need for reliable hydropedological information in Europe. In the last decades research institutes, universities and government agencies have developed local, regional and national datasets containing soil physical, chemical, hydrological and taxonomic information often combined with land use and landform data. A hydrological database for western European soils was also created in the mid-1990s. However, a comprehensive European hydropedological database, with possible additional information on chemical parameters and land use is still missing. A comprehensive joint European hydropedological inventory can serve multiple purposes, including scientific research, modelling and application of models on different geographical scales. The objective of the joint effort of the participants is to establish the European Hydropedological Data Inventory (EU-HYDI). This database holds data from European soils focusing on soil physical, chemical and hydrological properties. It also contains information on geographical location, soil classification and land use/cover at the time of sampling. It was assembled with the aim of encompassing the soil variability in Europe. It contains data from 18 countries with contributions from 29 institutions. This report presents an overview of the database, details the individual contributed datasets and explains the quality assurance and harmonization process that lead to the final database

Pore measurements on moraine topsoils using Computed Tomography which were sampled at Sustenpass and Klausenpass in the Swiss Alps in 2017

The sampling campaign took place in August/September 2017 as part of the project HILLSCAPE, Hillslope Chronosequence and Process Evolution. This dataset comprises different pore parameters measured by Computed Tomography. Parameters of the pores include volume, radius, area and equivalent diameter. The Skeleton files include graph, segment and node statistics

Numerical Simulation and Experimental Study of the Drop Impact for a Multiphase System Formed by Two Immiscible Fluids

The multiphase splash phenomenon is especially interesting in the context of environmental protection, as it could be a mechanism for transporting various types of pollution. A numerical 3D multiphase transport model was applied to a splash that occurred under the impact of a petrol drop on the water surface. The splash phenomenon in immiscible liquids was simulated using the multiphaseInterFoam solver, i.e., a part of the OpenFOAM computational fluid dynamics software implementing the finite volume method (FVM) for space discretization. Thirteen variants with a variable drop size (3.00–3.60 mm) or drop velocity (3.29–3.44 m/s) were conducted and validated experimentally based on splash images taken by a high-speed camera (2800 fps). Based on the numerical simulation, it was possible to analyse aspects that were difficult or impossible to achieve experimentally due to the limitations of the image analysis method. The aspects included the cavity spread, the jet forming moment, and, notably, the scale of the petroleum contamination spread in the splash effect. The simulations showed that droplets detaching from the crown did not consist of pure water but were mostly a “mixture” of water and petrol or petrol alone. The applied modelling workflow is an efficient way to simulate three-phase splash phenomena

Using SVM for soil water retention modelling

1 copia .pdf (a-3) del póster original presentado (2 Figs., 2 Tabls.)This work presents point pedotransfer function models of the soil water retention curve. Developed models allow for estimation of the soil water content based on following soil characteristics: soil granulometric composition, total porosity and bulk density. Soil water content is evaluated for the specified soil water potentials: -0.98 kPa, -3.10 kPa, -9.81 kPa, - 31.02 kPa, -491.66 kPa and -1554.78 kPa. Support Vector Machines (SVM) methodology was used for model development. Alternative to previous attempts ν-SVM method was used for models development and results compared with C-SVM based models. In the work two different types of the SVM kernel function was used and results compared: radial basis kernel function with the linear one. Soil properties used as input variables for the PTF models are: sand fraction, clay fraction, total porosity and bulk density. Models approximate value of the soil water content for seven fixed values of the soil water potential.Peer reviewe

Using SVM for soil water retention modelling

1 copia .pdf (a-3) del póster original presentado (2 Figs., 2 Tabls.)This work presents point pedotransfer function models of the soil water retention curve. Developed models allow for estimation of the soil water content based on following soil characteristics: soil granulometric composition, total porosity and bulk density. Soil water content is evaluated for the specified soil water potentials: -0.98 kPa, -3.10 kPa, -9.81 kPa, - 31.02 kPa, -491.66 kPa and -1554.78 kPa. Support Vector Machines (SVM) methodology was used for model development. Alternative to previous attempts ν-SVM method was used for models development and results compared with C-SVM based models. In the work two different types of the SVM kernel function was used and results compared: radial basis kernel function with the linear one. Soil properties used as input variables for the PTF models are: sand fraction, clay fraction, total porosity and bulk density. Models approximate value of the soil water content for seven fixed values of the soil water potential.Peer reviewe

Microstructural Differences in Response of Thermoresistant (Ceramic) and Standard (Granite) Concretes on Heating. Studies Using SEM and Nonstandard Approaches to Microtomography and Mercury Intrusion Porosimetry Data

The microstructure of concretes containing ceramic sanitary ware waste and granite aggregates was studied using scanning electron microscopy, mercury intrusion porosimetry and computer microtomography, before and after cyclic heating of the concretes to 1000 °C. All methods showed an increase in porosities in the concretes after heating. The proposed new approach to microtomography data analysis detected a much higher increase in the number of cracks in granite than in ceramic concrete after heating. This new approach to combining mercury intrusion and microtomography data showed that heating led to the narrowing of throats connecting smaller pore voids and a broadening of throats connecting larger pore voids, in both concretes

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models