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Gas pycnometry is based on Boyle-Mariotte's law of volume-pressure relationships. This method has been widely used to determine the volume (and thus the density) of rock fragments, soluble powders, light objects and even living beings. Surprisingly, little is known about the optimum design of gas pycnometers. The purpose of this study was to investigate the optimum design of a gas pycnometer, so that it can determine the volume of solid particles with the greatest accuracy. The 'constant-volume' gas pycnometer was considered because of its widespread use. The law of propagation of uncertainty was used to derive a theoretical formula that relates the pycnometer's accuracy to the main sources of random error (gas-pressure measurements, pycnometer temperature and sample-chamber volume). The consequences of this formula in terms of optimizing the geometry and working conditions of the pycnometer are discussed. It was found that some gas pycnometers described in the literature may have not been used under the best conditions. Guidelines are given to design a gas pycnometer that can theoretically determine the volume of solid particles with a relative standard uncertainty smaller than 0.2%.

Multilinear regression has been used extensively to predict soil hydraulic properties, both the θ(h) and K(h) relationships, from easily obtainable soil variables. As an alternative, this study investigated the performance of an artificial radial basis neural network in predicting some K(h) values from other variables. This kind of neural network may be seen as a multivariate interpolation technique, which can theoretically fit any nonlinear continuous function. Neural networks are characterized by parameters that must be optimized to solve a given problem. We used a fitting procedure requiring only two parameters to ensure a unique solution. These two parameters were determined by data splitting. Hypothetical data bases with uncertainties were simulated to analyze the performance of the neural network in predicting a nonlinear relation derived from a classical model for K(h). A soil data base covering a broad spectrum of soil horizons was used to test the neural network in solving multivariate problems. Numerical simulations showed that the neural network was sensitive to large uncertainties in the data base. It was more efficient than a multilinear regression when the uncertainties were small. Experimental results showed that the neural network was more efficient than the multilinear regression for predicting K(h = −1 m) or K(h = −2.5 m) from two qualitative and five quantitative soil variables. It was also more efficient than two independent multilinear regressions, one for the sandy samples and the other for the loamy and clayey samples. Provided that a large data base with accurate K values is available, artificial neural networks can be useful to predict θ(h) and K(h) over a broad spectrum of soils.

Abstract:To monitor the stage in turbid reservoirs with a sloping bank, it has been proposed to install a near-infrared Lidar on the bank and to orient it so that it points at the water surface with a large incidence angle (between ≈ 30 • and 70 • ). The technique assumes that the Lidar can detect suspended particles that are slightly below the water surface. Some laboratory results and the first long-term assessment (>2 years) of the technique are presented. It found that: (1) although the test Lidar provides erratic distance data, they can be easily filtered according to the intensity of the received signal; (2) the Lidar provides reliable data only when the water is very turbid (Secchi depth smaller than ≈ 1.0 m); and (3) the reliable data can be used to estimate daily stage values (after a simple field calibration) with an uncertainty better than ±0.08 m (p = 0.95). Although the present form of the technique is not very accurate, it uses an inexpensive instrument (≈1500 USD) which can be easily installed in a safe place (such as is the roof of a building). It is argued that the technique could be also used to monitor the stage and the sub-surface velocity in others turbid water bodies, such as some coastal areas (a recent field of application) and flooding rivers.

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