ﺑﻬﯿﻨﻪ ﺳﺎزي روشﻫﺎي ﺗﻮﺳﻌﻪ ﮐﻢ اﺛﺮ LID در ﮐﻨﺘﺮل ﮐﻤﯽ و ﮐﯿﻔﯽ ﺳﯿﻼب ﻫﺎي ﺷﻬﺮي ﺑﺎ ﻟﺤﺎظ اﺛﺮات ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢﻫﺎي ﺑﻬﯿﻨﻪ ﺳﺎز ﭼﻨﺪﻫﺪﻓﻪ

Optimizing Low Impact Development (LID) Methods in Urban Runoff Quantitative and Qualitative Control with Considering the Effects of Climate Change Using Multi-Objective Optimization Algorithms

ﻣﺪﯾﺮﯾﺖ ﮐﻤﯽ و ﮐﯿﻔﯽ رواﻧﺎب ﻗﺴﻤﺘﯽ از ﺷﻬﺮ ﮐﺮج ﺑﺎ ﮐﺎرﺑﺮد ﺑﻬﯿﻨﻪﺳﺎزي روشﻫﺎي ﺗﻮﺳﻌﻪ ﮐﻢ اﺛﺮ LID)( در ﺷﺮاﯾﻂ ﭘﺎﯾﻪ و ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ ﺑﺮرﺳﯽ ﺷﺪه اﺳﺖ. ﺟﻬﺖ ارزﯾﺎﺑﯽ اﺛﺮات ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ، ﻣﺪل CanESM2 ﺑﺎ دورهي ﭘﺎﯾﻪ )1985-2005( و دوره آﺗﯽ )2040- 2020( ﺗﺤﺖ ﺳﻨﺎرﯾﻮﻫﺎي RCP2.6 و RCP8.5 اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻣﺪلﺳﺎزي ﻫﯿﺪروﻟﯿﮑﯽ و ﻫﯿﺪروﻟﻮژﯾﮏ ﺗﻮﺳﻂ ﻧﺮماﻓﺰار SWMM اﻧﺠﺎم ﭘﺬﯾﺮﻓﺘﻪ و ﺑﺎ اﻟﮕﻮرﯾﺘﻢ ﻫﺎي ﭼﻨﺪﻫﺪﻓﻪ ازدﺣﺎم ذرات ) MOPSO( و ژﻧﺘﯿﮏ ﭼﻨﺪﻫﺪﻓﻪ ﺑﺎ ﻣﺮﺗﺐﺳﺎزي ﻧﺎﻣﻐﻠﻮب )NSGA-II( ﺗﻠﻔﯿﻖ ﺷﺪه اﺳﺖ. ﺗﻌﺪاد 37 زﯾﺮﺣﻮﺿﻪ و ﭘﻨﺞ ﻧﻮع LID ﺑﻪ اﻟﮕﻮرﯾﺘﻢﻫﺎ ﻣﻌﺮﻓﯽ ﺷﺪه اﺳﺖ. ﺟﻬﺖ ﻣﻘﺎﯾﺴﻪ و ارزﯾﺎﺑﯽ اﻟﮕﻮرﯾﺘﻢ ﻫﺎ، ﻫﺸﺖ ﺳﻨﺎرﯾﻮ ﺗﻌﺮﯾﻒ ﺷﺪ. در ﺷﺮاﯾﻂ ﭘﺎﯾﻪ، ﺑﺎ اﻋﻤﺎل اﻟﮕﻮرﯾﺘﻢﻫﺎي NSGA-II و MOPSO ﺑﻪ ﺗﺮﺗﯿﺐ 21/2 و 20 /3 درﺻﺪ ﮐﺎﻫﺶ ﺣﺠﻢ آبﮔﺮﻓﺘﮕﯽ در داﺧﻞ ﺣﻮﺿﻪ، 59/1 و 58/4 درﺻﺪ ﮐﺎﻫﺶ ﻏﻠﻈﺖ ﻣﻮاد ﺟﺎﻣﺪ ﻣﻌﻠﻖ )TSS(، 16/6 و 12/7 درﺻﺪ ﮐﺎﻫﺶ ﻏﻠﻈﺖ آﻻﯾﻨﺪه ﻧﯿﺘﺮوژن ﮐﻞ )TN( و 29/7 و 28/7 درﺻﺪ ﮐﺎﻫﺶ ﻏﻠﻈﺖ آﻻﯾﻨﺪه ﺳﺮب )Pb( در ﺧﺮوﺟﯽ ﺣﻮﺿﻪ ﺻﻮرت ﭘﺬﯾﺮﻓﺘﻪ اﺳﺖ. راهﺣﻞ ارﺋﻪ ﺷﺪه ﺗﻮﺳﻂ اﻟﮕﻮرﯾﺘﻢﻫﺎ در ﺷﺮاﯾﻂ ﺗﻐﯿﯿﺮ اﻗﻠﯿﻢ، از ﻧﻈﺮ ﮐﻤﯿﺖ ﺳﯿﻼب ﻣﺸﺎﺑﻪ ﺷﺮاﯾﻂ ﭘﺎﯾﻪ اﺳﺖ. غلظت TSS و با شرايط سناريوي RCP2.6، به ترتيب كاهشي برابر با 59/5 و55/2 درصد و در شرايط سناريوي RCP8.5، بهﺗﺮﺗﯿﺐ 59/6 و 55/8 درﺻﺪ ﺑﻪدﺳﺖ آﻣﺪ. ﺑﺮايTN ، در ﺷﺮاﯾﻂ ﺳﻨﺎرﯾﻮيRCP2.6 ، اﯾﻦ ﻣﻘﺎدﯾﺮ ﺑﻪ ﺗﺮﺗﯿﺐ 17/4 و 13/4 درﺻﺪ و در ﺳﻨﺎرﯾﻮيRCP8.5 ، ﺑﻪ ﺗﺮﺗﯿﺐ 17/6 و 13/5 درﺻﺪ ﮐﺎﻫﺶ داﺷﺘﻪاﻧﺪ . Pbدر ﺷﺮاﯾﻂ ﺳﻨﺎرﯾﻮي RCP2.6 ، ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺎ 30/1 و 29/7درﺻﺪ و در ﺳﻨﺎرﯾﻮي RCP8.5، ﺑﺎ 30/9 و 30/4 درﺻﺪ ﮐﺎﻫﺶ داﺷﺘﻪ اﺳﺖ

Quantitative and qualitative management of urban runoff in part of Karaj city has been investigated using optimization of Low Impact Development (LID) methods under base period and climate change conditions. CanESM2 was employed with the base period (1985-2005) and future period (2020-2040) under the RCP2.6 and RCP8.5 scenarios to investigate climate change impacts. Hydraulic and hydrological modeling was performed by Storm Water Management Model (SWMM) and was combined with Multi-objective Particle Swarm Optimization Algorithm (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II). Thirty-seven sub-catchments and five types of LIDS are introduced to the algorithms. Eight scenarios were defined to compare and evaluate the algorithms. Under the base period conditions, by applying NSGA-II and MOPSO algorithms, the flood volume in the catchment will decrease by 21.2% and 20.3%, total suspended solids (TSS) will increase by 59.1% and 58.4%, total nitrogen (TN) will increase by 16.6% and 12.7%, and lead (Pb) concentration will increase by 29.7% and 28.7%, respectively. Solution of the algorithms under climate change conditions gave similar flood values as the base conditions. In RCP2.6 scenario, TSS will decrease by 59.5% and 55.2%, respectively, and in RCP8.5 scenario, TSS will decrease by 59.6% and 55.8%, respectively. In RCP2.6 scenario, TN will decrease by 17.4% and 13.4%, respectively, and in RCP8.5 scenario, TN will decrease by 17.6% and 13.5%, respectively. Pb in RCP2.6 scenario will decrease by 30.1% and 29.7%, respectively, and in RCP8.5 scenario, Pb will decrease by 30.9% and 30.4%, respectively.