a b s t r a c tModern planning theories encourage approaches that consider all stakeholders with a variety of discourse values to avoid political and manipulative decisions. In the last decade, application of quantitative approaches such as multi-criteria decision making techniques in land suitability procedures has increased, which allows handling heterogeneous data. The majority of these applications mainly used decision-making techniques to rank the priority of predefined management options or planning scenarios. The presented study, however, shows how spatial decision-making can be used not only to rank the priority of options and performing scenario analysis, but also to provide insight into the spatial extent of the alternatives. This is particularly helpful in situation where political transitions in regard to urban planning policies leave local decision-makers with considerable room for discretion. To achieve this, the study compares the results of two quantitative techniques (analytical hierarchy procedure (AHP) and Fuzzy AHP) in defining the extent of land-use zones at a large scale urban planning scenario. The presented approach also adds a new dimension to the comparative analysis of applying these techniques in urban planning by considering the scale and purpose of the decision-making. The result demonstrates that in the early stage of the planning process, when identifying development options as a focal point is required, simplified methods can be sufficient. In this situation, selecting more sophisticated techniques will not necessarily generate different outcomes. However, when planning requires identifying the spatial extent of the preferred development area, considering the intersection area suggested by both methods will be ideal.

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This paper provides a framework for the assessment of the natural sensitivity of coastal lagoons to anthropogenic and other external inputs. The assessment framework is based on analysis and consideration of morphometric characteristics of coastal lagoons, and is demonstrated in this paper using eight example intermittently open coastal lagoons from New South Wales, Australia. The framework presented extends to a rudimentary classification of the eight example coastal lagoons, relative to the other 70 or so intermittently open coastal lagoons in NSW, which can be used to provide an indication of the relative importance of these lagoons with respect to future management, such as remediation or conservation.

Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation, from an offshore global swell model such as NOAA WaveWatch3, is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. (2006)) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves nearshore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.