بررسی الگوریتم بهینه‌سازی چندهدفه به منظور طراحی فضاهای شهری با رویکرد کنترل کمی-کیفی رواناب سطحی

از جمله عواملی که می‌تواند در کیفیت و کمیت رواناب‌های شهری تاثیر قابل توجهی بگذارد، رشد و توسعه‌ شهری و آگاهی از اثرات زیست محیطی آن بر جامعه می‌باشد. در سال‌های اخیر مفهوم جدیدی به نام روش‌های مدیریت مناسب سیلاب و با نام اختصاری bmps در راستای کنترل کمی و کیفی سیلاب‌های شهری مطرح شده است. در این مقاله سعی شده است با در نظرگرفتن سه تابع هدف کیفیت رواناب و کمیت رواناب و هزینه (شامل خسارت سیلاب و هزینه‌های نگهداری از bmps) ضمن مقایسه دو الگوی بهینه سازی nsgaii و mopso به ارائه‌ سناریو مناسب برای طراحی شهری رویکرد کنترل کمی و کیفی رواناب پرداخته شود. بر این اساس و با توجه به اهمیت مدیریت رواناب در کلانشهری مانند تهران، در این تحقیق قسمتی از حوضه آبریز منطقه 22 شهرداری تهران انتخاب و نسبت به ارزیابی اثرات bmpها بر روی کمیت رواناب با استفاده از مدل-های ریاضی بارش-رواناب اقدام شد. با مقایسه نتایج نسل آخر دو الگوریتم مشخص گردید که میانگین جواب‌های بهینه nsgaii بهینه‌تر و انحراف معیار جواب‌ها در نسل آخر نسبت به mopso بیشتر که این نشان‌دهنده‌ کارایی بهتر nsgaii می‌باشد اما استفاده از الگوریتم بهینه‌سازی mopso بدلیل دخیل بودن پارامترهای کمتری نسبت به nsgaii از سهولت بیشتری برخوردار خواهد بود. حداقل نمودن توابع هدف در ساختار پیشنهادی به عنوان هدف اصلی استفاده از این ابزار مطرح بوده است در الگوریتم nsga-ii بیشتر از mopso است. لازم به ذکر است رسیدن به حالت پایدار در nsgaii در تعداد نسل‌های کمتری نسبت به mopso صورت خواهد گرفت. همچنین نتایج حاصل از ارزیابی bmpها در قالب سناریوهای مختلف نشان داد که به کارگیری این راهکارها می‌تواند باعث کاهش دبی اوج از 16.3 به 50.1 درصد و نیز کاهش حجم رواناب از 9.2 تا 37.4 درصد بسته به نوع و تعداد bmp های به کار رفته در سطح حوضه شود. همچنین با بررسی سناریوهای منتخب نتیجه گردید که در بیش از 60 درصد از کاربری‌های مرتبط با فضای سبز، مخازن ماند بیولوژیکی و در کاربری‌های مسکونی و صنعتی کف‌پوش‌های نفوذپذیر و مخازن جمع‌آوری آب باران پیشنهاد شده است.

investigating multi-objective optimization algorithms for the design of urban spaces with a focus on quantitative and qualitative control of urban runoff

introduction surface runoff is considered one of the main components of the hydrological cycle and represents a vital source of water supply for various ecosystems and human activities. as urbanization continues to expand at an unprecedented rate in today's era, the transformation of permeable surfaces, such as soil and vegetation, into impervious surfaces like asphalt and concrete has led to significant adverse changes in both the quality and quantity of surface runoff. this shift not only alters the natural flow patterns but also increases the speed at which water travels over these surfaces, often resulting in higher volumes of runoff that can overwhelm drainage systems and lead to flooding. moreover, the quality of surface runoff has been negatively impacted, as it often carries pollutants such as heavy metals, oils, sediments, and nutrients from urban areas into nearby water bodies. this pollution poses serious threats to aquatic ecosystems and can compromise the safety of drinking water supplies. recognizing these challenges, flood management methods have been developed to harness this resource in a controlled manner, ensuring that it is utilized effectively to meet the increasing water demands of urban populations.through the application of these integrated approaches, communities can better manage their water resources while promoting sustainable urban development and preserving the health of local ecosystems.materials and methods based on this and considering the importance of runoff management in a metropolis like tehran, this research focuses on a specific segment of the catchment area within the 22nd district of tehran municipality. the study aims to evaluate the effects of best management practices (bmps) on the quantity and quality of runoff by employing advanced mathematical models that simulate precipitation and runoff dynamics. to comprehensively investigate this subject, the research incorporates three critical objective functions: first, runoff quality, which is assessed through parameters such as biochemical oxygen demand over five days (bod5) and total suspended solids (tss); second, runoff quantity, which involves measuring the volume of runoff generated in each sub-basin; and third, cost considerations, which encompass both flood damage costs and the maintenance expenses associated with implementing bmps. through this multifaceted approach, the study aims to compare the effectiveness of two optimization models—non-dominated sorting genetic algorithm ii (nsgaii) and multi-objective particle swarm optimization (mopso)—in achieving optimal runoff management solutions. by integrating these various dimensions, the research not only seeks to enhance the understanding of runoff behavior in urban environments but also aspires to contribute valuable insights for policymakers and urban planners in developing sustainable strategies for managing stormwater in tehran and similar metropolitan areas.results and discussionthe results of these two multi-objective evolutionary optimization algorithms conclude that the nsgaii optimization algorithm is more suitable due to the use of features such as crowding distance and the speed of performing different steps in the optimization algorithm. in addition, the use of mopso optimization algorithm will be easier due to the inclusion of fewer parameters than nsgaii. it is also necessary to mention that reaching the steady state in nsgaii will take place in fewer generations than mopso. also, the results of the evaluation of bmps in the form of different scenarios showed that the application of these solutions can reduce the peak discharge from 16.3% to 1.50% and also reduce the volume of runoff from 9.2% to 37.4% depending on the type and number of bmps used at the basin level. considering that, in general, the phenomenon of rainfall-runoff is a process that is strongly influenced by uncertain factors, and the inappropriate selection of design parameters leads to the incorrect estimation of the flood discharge and as a result, the selection of unfavorable dimensions for structures and technical performance becomes inappropriate or uneconomical. designs and ultimately financial and human losses will be many. therefore, the correct selection of design parameters is very important. conclusionin this research, after analyzing the uncertainty of the temporal and spatial distribution of rainfall as well as the initial moisture of the soil using the monte carlo simulation method and analyzing the sensitivity of the flood hydrograph to the continuation of the rainfall, flood management strategies in the region were thoroughly investigated. the results of the investigations showed that the highest peak flow is obtained from rainfall with a duration of 0.5 hours; in this case, the range of peak flow changes is equal to 34.8 cubic meters per second, which indicates the presence of high uncertainty in the input parameters of the rainfall-runoff model. in this regard, better results can be achieved by applying methods such as uncertainty analysis of inputs and effective parameters on the results of modeling or analyzing the sensitivity of the model to changing parameters. among the input factors of rainfall-runoff models that have a noticeable effect on the results, we can mention the temporal and spatial distribution of rainfall, the continuity of rainfall, and the previous soil moisture conditions. additionally, incorporating advanced statistical techniques and machine learning algorithms may further enhance predictive accuracy and optimize flood management strategies in urban environments, ultimately leading to more effective decision-making and improved resilience against flooding events.