Automated Floodway Determination Using Particle Swarm Optimization
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Mathematical Representation of the Problem
2.3. Isolated-Speciation-Based Particle Swarm Optimization
2.4. Automated Floodway Optimizer for HEC-RAS
Algorithm 1 Pseudocode for automated floodway optimization for HEC-RAS. | |
Require: itermax | ▹ Maximum number of iterations |
Require:M(·) | ▹ HEC-RAS model with 100-year and floodway plans and profiles |
Require: BC ∈ {None, DS, US, Both} | ▹ Boundary conditions for the encroachment limits |
Extract cross section information from M(·) | |
Number of cross sections | |
Number of boundary conditions | |
▹ Problem dimension | |
▹ Swarm size | |
Afw,min, Afw,max ← Minimum and maximum possible areas of the floodway | |
X number of D-tuples randomly sampled from | ▹ Initial population |
Let that maps particles to encroachment limits | |
iter | |
repeat | ▹ ISPSO loop |
for do | |
Xi ← Row i from X | ▹ trial encroachment limits or particle i in ISPSO |
Simulate M(g(Xi)) using CLIRAS | ▹ Execute the HEC-RAS program |
Evaluate f (M(g(Xi))) | ▹Equation (1) |
if or f (M(g(Xi))) < f (M(g(Xbest))) then | ▹ If Xi is better than Xbest |
Xbest ← Xi | ▹ Store the best encroachment limits from the current iteration |
end if | |
end for | |
if iter = 1 or f (M(g(Xbest))) < f (M(g(xbest))) then | ▹ If Xbest is better than xbest |
xbest ← Xbest | ▹ Store the best encroachment limits so far |
end if | |
Evolve X using ISPSO | ▹ Evolution of the swarm in ISPSO |
iter ← iter + 1 | |
until iter = itermax or other conditions are satisfied | |
Optimized encroachment limits (xbest) | ▹ Found the best encroachment limits |
2.5. Numerical Experiments
3. Results and Discussion
3.1. Comparison of Different Approaches
3.2. Sensitivity of Encroachment Limits to the Boundary Condition
3.3. Optimization Performance
3.4. AFORAS as a Tool for Floodway Optimization
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Trial | (m) | (m) | Violations | Equation (1) | Equation (2) | |||
---|---|---|---|---|---|---|---|---|
1 | 50 | 0.366 | 0.397 | 1.1 | 1.2 | 4 | 1.80 | 0.50 |
2 | 60 | 0.336 | 0.366 | 1.0 | 0.8 | 3 | 1.30 | 0.30 |
3 | 70 | 0.310 | 0.320 | 1.1 | 0.9 | 1 | 1.17 | 0.07 |
4 | 80 | 0.295 | 0.295 | 0.9 | 0.7 | 0 | 0.60 | 0.07 |
5 | 90 | 0.244 | 0.214 | 0.8 | 0.6 | 0 | 0.80 | 0.50 |
6 | 100 | 0.305 | 0.305 | 0.9 | 0.8 | 0 | 1.00 | 0.00 |
BC | Problem Dimension | AFORAS | Manual | HEC-RAS |
---|---|---|---|---|
None | 24 | 0.270 | 0.348 (29%) | 0.379 (40%) |
DS | 22 | 0.278 | 0.345 (24%) | 0.379 (36%) |
US | 22 | 0.333 | 0.347 (4%) | 0.379 (14%) |
Both | 20 | 0.338 | 0.342 (1%) | 0.379 (12%) |
BC | XS 8.05 km | XS 8.15 km | XS 8.26 km–9.27 km | XS 9.45 km | XS 9.64 km |
---|---|---|---|---|---|
None | 5a | 5b | 5c–5j | 5k | 5l |
DS | 6a | ∼6b | ∼5c–5j | ∼5k | ∼5l |
US | ∼5a | ∼5b | ∼5c–5j | ∼6k | 6l |
Both | 6a | 6b | 6c–6j | 6k | 6l |
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Cho, H.; Yee, T.M.; Heo, J. Automated Floodway Determination Using Particle Swarm Optimization. Water 2018, 10, 1420. https://doi.org/10.3390/w10101420
Cho H, Yee TM, Heo J. Automated Floodway Determination Using Particle Swarm Optimization. Water. 2018; 10(10):1420. https://doi.org/10.3390/w10101420
Chicago/Turabian StyleCho, Huidae, Tien M. Yee, and Joonghyeok Heo. 2018. "Automated Floodway Determination Using Particle Swarm Optimization" Water 10, no. 10: 1420. https://doi.org/10.3390/w10101420
APA StyleCho, H., Yee, T. M., & Heo, J. (2018). Automated Floodway Determination Using Particle Swarm Optimization. Water, 10(10), 1420. https://doi.org/10.3390/w10101420