At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

A novel data analysis approach that is automatic, self-learning and self-explained, and which provides accurate and reliable results is reported. The data analysis tool is capable of performing multivariate non-parametric regression analysis, as well as quantitative inferential analysis using predictive learning. Statistical approaches such as multiple regression or discriminant analysis are usually used to perform this kind of analysis. However, they lack universal capabilities and their success in any particular application is directly affected by the problem complexity.The approach employs the use of Artificial Neural Networks (ANNs) and Fuzzy Logic to perform the data analysis. The features of these two techniques are the means by which the developed data analysis approach has the ability to perform self-learning as well as allowing user interaction in the learning process. Further, they offer a means by which rules may be generated to assist human understanding of the learned analysis model, and so enable an analyst to include external knowledge.Two problems in the resource industry have been used to illustrate the proposed method, as these applications contain non-linearity in the data that is unknown and difficult to derive. They are well log data analysis in petroleum exploration and hydrocyclone data analysis in mineral processing. This research also explores how this proposed data analysis approach could enhance the analysis process for problems of this type.

The Presented Thesis is focused on exploitation of stochastic cyclical networks in project management during project planning. Particularly, it is focused on the GERT method, which enables to carry out both the probability analysis and the time analysis of projects with stochastic structure. We deal primarily with analysis of such networks where cyclical activities occur. As an integral part of the Thesis, derivation of simplified computing procedures for cyclical activities is included. We extend the possibilities of the GERT method with stochastic evaluation of time duration of activities using the fuzzy GERT method. This fuzzy GERT method is applied on the real project and its results are compared to results of Monte Carlo simulation.