Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction

2006 ◽  
Vol 17 (6) ◽  
pp. 635-652 ◽  
Author(s):  
Dale L. Zimmerman
Keyword(s):  
Parameter Estimation ◽  
Network Design ◽  
Spatial Prediction ◽  
Empirical Prediction ◽  
Optimal Network Design ◽  
Optimal Network

A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines

2019 ◽  
Vol 49 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Ahmad Hosseini ◽  
Ola Lindroos ◽  
Eddie Wadbro
Keyword(s):  
Network Design ◽  
Multiple Objectives ◽  
Optimization Framework ◽  
Trade Offs ◽  
Design Changes ◽  
Optimal Network Design ◽  
Optimal Network ◽  
Ecological Objectives ◽  
Holistic Optimization ◽  
The Impact

Ground-based mechanized forestry requires the traversal of terrain by heavy machines. The routes that they take are often called “machine trails” and are created by removing trees from the trail and placing the logs outside it. Designing an optimal machine trail network is a complex locational problem that requires understanding how forestry machines can operate on the terrain, as well as the trade-offs between various economic and ecological aspects. Machine trail designs are currently created manually based on intuitive decisions about the importance, correlations, and effects of many potentially conflicting aspects. Badly designed machine trail networks could result in costly operations and adverse environmental impacts. Therefore, this study was conducted to develop a holistic optimization framework for machine trail network design. Key economic and ecological objectives involved in designing machine trail networks for mechanized cut-to-length operations are presented, along with strategies for simultaneously addressing multiple objectives while accounting for the physical capabilities of forestry machines, the impact of slope, and the operating costs. Ways of quantitatively formulating and combining these different aspects are demonstrated, together with examples showing how the optimal network design changes in response to various inputs.

scholarly journals Optimal design of a climatological network: beyond practical considerations

2013 ◽  
Vol 2 (2) ◽  
pp. 199-212 ◽  
Author(s):  
G. S. Mauger ◽  
K. A. Bumbaco ◽  
G. J. Hakim ◽  
P. W. Mote
Keyword(s):  
Network Design ◽  
Pacific Northwest ◽  
Spatial Scales ◽  
Optimal Placement ◽  
Optimal Network Design ◽  
Optimal Network ◽  
The Us ◽  
Mean Climate ◽  
Expert Input ◽  
Practical Constraints

Abstract. Station locations in existing environmental networks are typically chosen based on practical constraints such as cost and accessibility, while unintentionally overlooking the geographical and statistical properties of the information to be measured. Ideally, such considerations should not take precedence over the intended monitoring goal of the network: the focus of network design should be to adequately sample the quantity to be observed. Here we describe an optimal network design technique, based on ensemble sensitivity, that objectively locates the most valuable stations for a given field. The method is computationally inexpensive and can take practical constraints into account. We describe the method, along with the details of our implementation, and present-example results for the US Pacific Northwest, based on the goal of monitoring regional annual-mean climate. The findings indicate that optimal placement of observing stations can often be highly counterintuitive, thus emphasizing the importance of objective approaches. Although at coarse scales the results are generally consistent, sensitivity tests show important differences, especially at smaller spatial scales. These uncertainties could be reduced with improvements in datasets and improved estimates of the measurement error. We conclude that the method is best suited for identifying general areas within which observations should be focused, and suggest that the approach could serve as a valuable complement to land surveys and expert input in designing new environmental observing networks.

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