TY - JOUR
T1 - Optimization approach for 4-D natural landscape evolution
AU - Paik, Kyungrock
N1 - Funding Information:
Manuscript received September 23, 2009; revised April 3, 2010 and July 26, 2010; accepted September 14, 2010. Date of current version September 30, 2011. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant 2010-0006636.
PY - 2011/10
Y1 - 2011/10
N2 - This paper presents a unique optimization method developed for landscape evolution problems. An existing hypothesis of the optimal channel network states that fluvial landscape evolution can be characterized as the procedure that follows minimum total energy expenditure. Previous studies have tested this hypothesis by solving an optimization problem, i.e., finding landscapes that satisfy the minimum total energy expenditure criterion, and showed that such optimized landscapes are similar to natural landscapes in many respects. These studies have approximated a 3-D landscape as a 2-D river network. While this network-based approach has greatly simplified the formulation of the optimization problem, this approximation limits the investigation of features such as longitudinal profiles, since their representation requires the gravitational direction-wise dimension. Here, an alternative technique is devised to fully handle the optimization of 3-D landforms over time. The proposed idea is to break down the time domain and to apply an optimization algorithm sequentially for discrete time steps. For the optimization part, a heuristic algorithm motivated from adaptation strategies of natural systems (here landscape formation) is used. This method is applied to a theoretical landscape with the condition that the balance between tectonic uplift and sediment lost is satisfied. It is found that landscapes of minimum total energy expenditure exhibit the Hack's law and the power-law in the exceedance probability distribution of drainage area, which are the characteristics found in natural river networks. However, they demonstrate no systematic pattern in longitudinal profiles.
AB - This paper presents a unique optimization method developed for landscape evolution problems. An existing hypothesis of the optimal channel network states that fluvial landscape evolution can be characterized as the procedure that follows minimum total energy expenditure. Previous studies have tested this hypothesis by solving an optimization problem, i.e., finding landscapes that satisfy the minimum total energy expenditure criterion, and showed that such optimized landscapes are similar to natural landscapes in many respects. These studies have approximated a 3-D landscape as a 2-D river network. While this network-based approach has greatly simplified the formulation of the optimization problem, this approximation limits the investigation of features such as longitudinal profiles, since their representation requires the gravitational direction-wise dimension. Here, an alternative technique is devised to fully handle the optimization of 3-D landforms over time. The proposed idea is to break down the time domain and to apply an optimization algorithm sequentially for discrete time steps. For the optimization part, a heuristic algorithm motivated from adaptation strategies of natural systems (here landscape formation) is used. This method is applied to a theoretical landscape with the condition that the balance between tectonic uplift and sediment lost is satisfied. It is found that landscapes of minimum total energy expenditure exhibit the Hack's law and the power-law in the exceedance probability distribution of drainage area, which are the characteristics found in natural river networks. However, they demonstrate no systematic pattern in longitudinal profiles.
KW - Extremal hypotheses
KW - genetic algorithms
KW - landscape evolution
KW - optimal channel network
KW - optimization methods
UR - http://www.scopus.com/inward/record.url?scp=80054703297&partnerID=8YFLogxK
U2 - 10.1109/TEVC.2010.2087027
DO - 10.1109/TEVC.2010.2087027
M3 - Article
AN - SCOPUS:80054703297
SN - 1089-778X
VL - 15
SP - 684
EP - 691
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
IS - 5
M1 - 6035968
ER -