Ecological determinism in Australia : tracing the legacy of McHarg downunder and its key precedents

Ecological planning, as advocated by Ian McHarg, filtered extensively through North America following the publication of Design with Nature (1965). The integrated design and planning approach was also advanced by numerous graduates of McHarg's studios at the University of Pennsylvania where this approach was extensively trialled and proven. While a clear synthesis and theoretical framework was articulated and reinforced through a plethora of projects, monographs, and articles, the majority of these perspectives were North American, lacked clarity about the translation of the approach into legal strategic and statutory planning instruments, nor shed light upon what transpired in Australia. This paper reviews the development of the Conservation Plan created for the southern Mornington peninsula in Victoria, Australia, as well as its intent, structure and internal workings as a successful model of ecological statutory planning, in the context of the wider WPRPA activities that draws directly from the McHarg theory. Known as the Conservation Plan for the southern Mornington Peninsula in Victoria, a revolutionary planning structure devised in the early 1970s by several Australian proponents. The Conservation Plan continues in operation today curating a high scenic valued landscape protecting it from intrusion from the growing metropolitan city of Melbourne thus fulfilling its objectives of landscape quality conservation whilst still permitting sympathetic building and land use growth. Contextually, the Conservation Plan appears to be only statutory equivalent translation of the approach internationally other than the Pinelands Commission planning processes in New Jersey.

Approaching cultural landscapes in post-settler societies: ideas, policies, practices

This thesis contends that the concept of cultural landscape is a useful tool for dismantling heritage management programs that promote demarcations between natural/settler/indigenous heritage values in protected areas in New Zealand, Australia, Canada and the United States.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.