A methodology for creating greenways through multidisciplinary sustainable landscape planning

This research proposes a methodology for defining greenways via sustainable planning. This approach includes the analysis and discussion of culture and natural processes that occur in the landscape. The proposed methodology is structured in three phases: eco-cultural analysis; synthesis and diagnosis; and proposal.An interdisciplinary approach provides an assessment of the relationships between landscape structure and landscape dynamics, which are essential to any landscape management or land use. The landscape eco-cultural analysis provides a biophysical, dynamic (geomorphologic rate), vegetation (habitats from directive 92/43/EEC) and cultural characterisation. The knowledge obtained by this analysis then supports the definition of priority actions to stabilise the landscape and the management measures for the habitats. After the analysis and diagnosis phases, a proposal for the development of sustainable greenways can be achieved.This methodology was applied to a study area of the Azambuja Municipality in the Lisbon Metropolitan Area (Portugal). The application of the proposed methodology to the study area shows that landscape stability is crucial for greenway users in order to appreciate the landscape and its natural and cultural elements in a sustainable and healthy way, both by cycling or by foot. A balanced landscape will increase the value of greenways and in return, they can develop socio-economic activities with benefits for rural communities.     

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torr?o reservoir (Tamega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.