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By Jos P Van Leeuwen
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Extra info for Recent advances in design and decision support systems in architecture and urban planning
THE PREDICTION CAPABILITIES OF THE SANN Once the learning and testing phase has been concluded, the averaged weight matrix of the SANNs is processed with the Data set of cells “potentially” in urbanisation in the next time lag (1994-2008). The prediction concerns “green” cells with urbanized neighbourhood in1994. Figure 8 shows the estimated surfaces for each land use. As expected, the trend is linear, given the availability of only two temporal thresholds. The predicted land use growth The resulted pattern shows a probable scenario (Fig.
In order to test the generalization capability of the models, the total number of trips associated to the actual origins and destinations taken from those records were applied as input data in the trained networks for estimating the 2001 flows. Table 2. Absolute number of records in the subsets used for training, validation, and query in the case of public day-care centers and EMEIs Facilities Year Training Validation Query Total Day-care centers 2000 1225 613 612 2450 2001 2695 2695 EMEIs 2000 2695 1348 1347 5390 2001 5635 5635 Before introducing the data in the ANN, they were normalized as shown in Equations (1) and (2).
In this paper, a case study was used to show the practical application of the proposed model. In the Hendek region of Turkey, a section of a natural gas pipeline was broken due to landslides and 19 probable factors causing landslides were taken into hand to prepare the landslide susceptibility map for re-routing studies. Neural networks were used in the analysis stage of the study. 12% overall accuracy with the test dataset. 42%) are quite high. 89. Moreover, critical slope faces observed in the field were checked in the resultant map and most of them fall into high or very highly susceptible zones.
Recent advances in design and decision support systems in architecture and urban planning by Jos P Van Leeuwen