The search for suitable marine areas is a prerequisite at an early stage of any offshore renewable energy system project. This paper introduces a methodological framework for identifying the maritime areas the most appropriate to implement an offshore wind turbine farm in the Hong Kong bay. The framework is based on the combined use of Multi-Criteria Decision Analysis (MCDA) and Geographical Information Systems (GIS). At the first stage of the analysis, unsuitable areas for the deployment of an offshore wind turbine have been first identified. At the second stage of the analysis, eligible marine areas have been ranked using a MCDA and different scenarios. Finally, a cost analysis has been performed to refine the whole approach as well as a comparison to previous studies in the Hong Kong bay.

a b s t r a c tThe research presented in this paper is part of a project whose aim is to develop a flexible system to help industrials to efficiently install marine energy farms in a suitable area. We introduce a methodology and a decision-aid system for marine farm design. The developed framework will help, for a given marine area, to find the most relevant sites and marine technologies using multi-criteria analysis. The approach is oriented toward marine current energy but this methodology can be extended to others marine energies. Amongst the criteria involved in the decision process, a model is developed to evaluate the quantity of electrical energy produced by the farm, and the cost of the system during its entire lifetime. These two parameters allow us to derive the cost of the produced energy, which is one of the more important criteria to evaluate the economic feasibility of a marine energy project. The energy produced is evaluated taking into account both technological possibilities (turbines technology, generator type, underwater cables, offshore substation etc.) and site characteristics. The cost is estimated thanks to a specific cost model of each component and the farm layout, and also includes a first order evaluation of the installation/dismantling and maintenance costs.

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This study involves multifractal characterization of pockmark seepage associated seafloor along the central part of the western continental margin of India (WCMI). Six representative blocks of backscatter and bathymetry co-registered image data were used to characterize the seafloor. Two distinct multifractal formalisms were applied to determine the characteristics. The first formalism employs data analyses using generalized dimension D(q) and multifractal singularity spectrum f(α) linked shape parameters, based on the 'strange' attractors that exhibit multifractal scaling. The second approach is designed as 'stochastic' multifractal fields that connect the image block quantification to the three fundamental parameters namely, degree of multifractality α, sparseness C 1 and degree of smootheness H. The present investigation using the two multifractal formalisms to characterize the seafloor backscatter and bathymetry data provides comparative results that can be expounded upon.