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Performance Improved Multi-Objective Optimization in Applying Low-Impact Development Strategies to Control Urban Runoff | ||
Civil Engineering Infrastructures Journal | ||
دوره 56، شماره 2، اسفند 2023، صفحه 257-276 اصل مقاله (707.71 K) | ||
نوع مقاله: Research Papers | ||
شناسه دیجیتال (DOI): 10.22059/ceij.2022.342561.1840 | ||
نویسندگان | ||
Hossein Naghibzadeh1؛ Mohsen Saadat* 2؛ Shamsa Basirat2؛ Mehran Iranpour Mobarakeh3 | ||
1Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. | ||
2Assistant Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. | ||
3Assistant Professor, Department of Civil Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran. | ||
چکیده | ||
Best Management Practices (BMPs) can play a vital role to control natural disasters like floods. In this paper, retention pond and vegetative swale are considered to restrain urban runoff. Storm water management modeling (SWMM) is used for runoff modeling. A piece of code is developed based on Non-dominated Sorting Genetic Algorithm (NSGA-II) in MATLAB to optimize the BMPs application. The aim is comparing the effect of roulette wheel, tournament and random selection operators to obtain the optimal location and area of BMPs. Minimizing the runoff volume and pollution in sub-catchments and the construction cost of the BMPs are three objective functions. Rafsanjan city located in southeast of Iran is selected as an appropriate case study. Estimating the best pressure of selection operator in roulette wheel and the best selection size in tournament operator and simultaneous quantitative and qualitative optimization using two BMPs are the innovations of this study. The results indicate that the pressure of the selection operator in roulette wheel which leads to the optimal answer is three and nine while the best size of selection in the tournament operator is nine. Optimum location, type, area and volume for each BMP are obtained after running the code. | ||
کلیدواژهها | ||
Best Management Practices؛ Roulette Wheel؛ Selection Operators؛ SWMM؛ Tournament | ||
مراجع | ||
Abou Rjeily, Y., Abbas, O., Sadek, M., Shahrour, I. and Hage Chehade, F. (2018). "Model predictive control for optimising the operation of urban drainage systems", Journal of Hydrology, 566, 558-565, https://doi.org/10.1016/j.jhydrol.2018.09.044.
Alaneme, G.U., Dimonyeka, M.U., Ezeokpube, G.C., Uzoma, I.I. and Udousoro, I.M. (2021). "Failure assessment of dysfunctional flexible pavement drainage facility using fuzzy analytical hierarchical process", Innovative Infrastructure Solutions, 6(2), 1-18, https://doi.org/10.1007/s41062-021-00487-z.
Bayou Land RC&D and Louisiana Public Health Institute. (2010). Stormwater BMP guidance tool, DEQ, Lousiana.
Bell, V.A. and Moore, R.J. (2000). "The sensitivity of catchment runoff models to rainfall data at different spatial scales", Hydrology and Earth System Sciences, 4(4), 653-667, https://doi.org/10.5194/hess-4-653-2000.
Binesh, N., Niksokhan, M.H., Sarang, A. and Rauch, W. (2019). "Improving resilience of urban drainage system in adaptation to climate change (Case study: Northern Tehran, Iran)", 16th International Environmental Specialty Conference 2018, Held as Part of the Canadian Society for Civil Engineering Annual Conference 2018, 16, 87-97.
Chetan, J.S. and Nitesh, M.S. (2021). "Genetic algorithm for test suite optimization: An experimental investigation of different selection methods", Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 3778-3787, https://doi.org/10.17762/turcomat.v12i3.1661.
Chudasama, C., Shah, S.M. and Panchal, M. (2011). "Comparison of parents selection methods of Genetic Algorithm for TSP", International Conference on Computer Communication and Networks CSI- COMNET, 1, 102-105.
Dastorani, M.T., Mahjoobi, J., Talebi, A. and Fakhar, F. (2018). "Application of machine learning approaches in rainfall-runoff modeling (Case study: Zayandeh Rood basin in Iran)", Civil Engineering Infrastructures Journal,1(2), 293-310, https://doi.org/10.7508/ceij.2018.02.004.
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 6(2), 182-197, https://doi.org/10.1109/4235.996017.
Gad, M., sobeih, M.F., Rashwan, I.M.H. and Helal, E. (2020). "Hydraulic features of flow through grassed canal", Innovative Infrastructure Solutions, 5(2), 1-14, https://doi.org/10.1007/s41062-020-00308-9.
Hai, D.M. (2020). "Optimal planning of low-impact development for TSS Control in the upper area of the Cau Bay River basin, Vietnam", Water (Switzerland), 12(2), 1-15, https://doi.org/10.3390/w12020533.
Hakimi, H., Ahmadi, J., Vakilian, A., Jamalizadeh, A., Kamyab, Z., Mehran, M., Malekzadeh, R., Poustchi, H., Eghtesad, S., Sardari, F., Soleimani, M., Khademalhosseini, M., Abolghasemi, M., Mohammadi, M., Sadeghi, T., Ayoobi, F., Abbasi, M., Mohamadi, M., Jalali, Z., Shamsizadeh, A. and Esmaeili-Nadimi, A. (2021). "The profile of Rafsanjan cohort study", European Journal of Epidemiology, 36(2), 243-252, https://doi.org/10.1007/s10654-020-00668-7.
Höschel, K. and Lakshminarayanan, V. (2019). "Genetic algorithms for lens design: A review", Journal of Optics (India), 48(1), 134-144, https://doi.org/10.1007/s12596-018-0497-3.
Huang, S., Liu, P., Zhang, H. and DIng, Z. (2021). "Research on SWMM runoff control index decomposition based on constraint optimization method", 3rd International Symposium on Architecture Research Frontiers and Ecological Environment (ARFEE 2020), France, https://doi.org/10.1051/e3sconf/202123704008.
Jamshidi, S., Imani, S. and Delavar, M. (2020). "Impact assessment of Best Management Practices (BMPs) on the water footprint of agricultural productions", International Journal of Environmental Research, 14(6), 641-652, https://doi.org/10.1007/s41742-020-00285-y.
Katoch, S., Chauhan, S.S. and Kumar, V. (2021). "A review on genetic algorithm: Past, present, and future", Multimedia Tools and Applications, 80, 8091-8126, https://doi.org/10.1007/s11042-020-10139-6.
Kumar, A., Kumar, D. and Jarial, S. K. (2016). "A comparative analysis of selection schemes in the artificial bee colony algorithm" ,Computacion y Sistemas, 20(1), 55-66, https://doi.org/10.13053/cys-20-1-2228.
Li, R. and Kuo, Y.M. (2021). "Effects of shallow water table depth on vegetative filter strips retarding transport of nonpoint source pollution in controlled flume experiments", International Journal of Environmental Research, 15(1), 163-175, https://doi.org/10.1007/s41742-020-00305-x.
Martínez-Solano, F.J., Iglesias-Rey, P.L., Saldarriaga, J.G. and Vallejo, D. (2016). "Creation of an SWMM toolkit for its application in urban drainage networks optimization", Water (Switzerland), 8(6), 1-16, https://doi.org/10.3390/w8060259.
Moosavian, N. and Jaefarzadeh, M.R. (2015). "Particle Swarm Optimization for hydraulic analysis of water distribution systems", Civil Engineering Infrastructures Journal, 48(1), 9-22, https://doi.org/10.7508/ceij.2015.01.002.
Ochoa-Barragán, R., Nápoles-Rivera, F. and Ponce-Ortega, J.M. (2021). "Optimal and fair distribution of water under water scarcity scenarios at a macroscopic level", International Journal of Environmental Research, 15(1), 57-77, https://doi.org/10.1007/s41742-020-00297-8.
Pennsylvania Department of Environmental Protection Bureau of Watershed Management. (2006). PENNSYLVANIA stormwater BMP manual, Water, Pennsylvania.
Rathnayake, U. (2015). "Migrating storms and optimal control of urban sewer networks", Hydrology, 2(4), 230-241, https://doi.org/10.3390/hydrology2040230.
Rossman, L. (2017). Storm water management model reference manual, Volume II: Hydraulics, Environmental Protection Agency, Cincinnati.
Sharma, P., Wadhwa, A. and Komal, K. (2014). "Analysis of selection schemes for solving an optimization problem in Genetic Algorithm", International Journal of Computer Applications, 93(11), 1-3, https://doi.org/10.5120/16256-5714.
Shukla, A., Pandey, H.M. and Mehrotra, D. (2015). "Comparative review of selection techniques in genetic algorithm", 1st International Conference on Futuristic Trends in Computational Analysis and Knowledge Management (ABLAZE-2015), Noida, https://doi.org/10.1109/ABLAZE.2015.7154916
Siriwardene, N.R. and Perera, B.J.C. (2006). "Selection of genetic algorithm operators for urban drainage model parameter optimisation", Mathematical and Computer Modelling, 44(5-6), 415-429, https://doi.org/10.1016/j.mcm.2006.01.002.
Swathi, V., Srinivasa Raju, K., Murari R.R.V. and Sai Veena, S. (2019). "Automatic calibration of SWMM using NSGA-III and the effects of delineation scale on an urban catchment", Journal of Hydroinformatics, 21(5), 781-797, https://doi.org/10.2166/hydro.2019.033.
Taban, M.H., Hajiazizi, M. and Ghobadian, R. (2021). "Prediction of Q-value by multi-variable regression and novel Genetic Algorithm based on the most influential parameters", Civil Engineering Infrastructures Journal, 54(2), 267-280, https://doi.org/10.22059/CEIJ.2020.295339.1647.
Tayfur, G. (2017). "Modern optimization methods in water resources planning, engineering and management", Water Resources Management, 31(10), 3205-3233, https://doi.org/10.1007/s11269-017-1694-6.
Wang, Q., Zhou, Q., Lei, X. and Savić, D.A. (2018). "Comparison of multiobjective optimization methods applied to urban drainage adaptation problems", Journal of Water Resources Planning and Management, 144(11), 04018070, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000996.
Wanielista, M. (2007). Regional stormwater irrigation facilities, Stormwater Management Academy University of Central Florida Orlando, Florida.
Xavier, C.R., Dos Santos, E.P., Da Fonseca Vieira, V. and Dos Santos, R.W. (2013). "Genetic algorithm for the history matching problem", Procedia Computer Science, 18, 946-955, https://doi.org/10.1016/j.procs.2013.05.260.
Xiong, J., Chen, B., He, Z., Guan, W. and Chen, Y. (2021). "Optimal design of community shuttles with an adaptive-operator-selection-based genetic algorithm", Transportation Research Part C: Emerging Technologies, 126, 1-37, https://doi.org/10.1016/j.trc.2021.103109. | ||
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