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Soil hydraulic properties are very time-consuming and expensive to measure directly. Conversely, routinely collected soil survey parameters (e.g. soil texture, dry bulk density and organic matter content) are relatively cheap and easy to collect. Fortunately, mathematical regression models called Pedotransfer Functions (PTFs) allow the transfer of data we have (soil survey parameters) into the data we need (soil hydraulic properties), and this thesis focusses on the potential and modelling methodologies of Artificial Neural Network (ANN) ensembles as a means by which to construct PTFs. The individual processes of the ensemble modelling procedure are examined herein, and suggestions as to how they may be improved upon, by mathematical, conjectural and empirical means, are discussed. Ultimately, it will show that more accurate, robust and efficient PTFs may be constructed by incorporating these into such models. An ensemble is a collection of individual ANNs, each of which provides an independent solution to the same problem. Thus, the combined strengths of the individual models are augmented, whilst their weaknesses are diminished. For any modelling methodology, a trade-off exists between the bias and variance components of the model error - decreasing the bias results in higher variance, and vice versa. However, for the ensemble method, combining ensemble members results in the reduction or elimination of the variance, whilst leaving the bias unaltered. Thus, the aim of the ensemble modeller is to determine an optimum balance between achieving low bias (regardless o f the consequences to the variance) and conserving data - a trade-off between the amount of data used and the value of that data. It has been empirically demonstrated here that by training ANNs using data selected such that all parts of the dataspace are equally represented, bias in ANN-PTF ensembles may be reduced to negligible levels. In addition, results showed that, using one-third or less the amount of data, the ensemble method yields results that are at least as accurate as single ANN methods.

A new technique has been developed to enable analyses of δ170 and δ180 from small water samples extracted from meteorites. Isotopic measurements are made using a continuous-flow, isotope ratio mass spectrometer (Delta C, Finnigan Mat) utilising a helium carrier gas. Oxygen is extracted from water using the powdered, solid fluorinating agent CoF3, and is purified using a GC column (PLOT, 5Å). Reproducibility of the method, determined using solid standards and reference waters is ±.30%c for δ18O and +0.14%c for ΔI7O (1σ in each case). The technique has been applied to three suites of meteorites: Carbonaceous chondrites (CI and CM), SNC meteorites (four meteorites) and a selection of Antarctic samples, three eucrites and one ordinary chondrite. Results were used to constrain models of the origin of Solar System water and its subsequent interaction on the meteorite parent bodies. Results from carbonaceous chondrites suggest that water accreting to bodies in the early Solar System was enriched in both 17O and 18O and had a Δ170 of at least +2%c The isotopic composition of water evolved during hydrothermal alteration on parent bodies toward lower Δ17O values, more similar to that of the host rocks. The complex release profile indicate that these meteorites experienced several periods of hydrothermal activity. SNC sample results have suggested the presence of two isotopically distinct reservoirs, the silicate crust and the hydrosphere each possessing distinct Δ170 values. A third component with a large 17O excess (+4%) was detected in ALH84001. The magnitude of this anomaly indicates an origin due to heterogeneous accretion or late stage veneer and cannot be produced by hydrodynamic escape. Results from the eucrites and ordinary chondrite also suggested isotopically distinct parent body silicate and hydrous reservoirs and may also be consistent with widespread late input of isotopically distinct water in the Solar System.