نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات
2 استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات
چکیده
ایران یکی از کشورهایی است که در معرض سوانح طبیعی بسیاری قرار دارد که سیل یکی از جدیترین آنهاست. چگونگی پایش و کنترل سوانح، ارزیابی خسارت و امدادرسانی از مهمترین مشکلات دولت و کارشناسان مدیریت بحران محسوب میشوند. در صورت نظارت مستمر قبل از وقوع، ارزیابی دقیق در حین و بعد از وقوع سانحه، میتوان از دامنه خسارات و هدررفت منابع انسانی و مادی جلوگیری کرد. جلوگیری از خطرات ناشی از سیل، ساماندهی و مدیریت سیل در رودخانهها و نهایتاً بهسازی رودخانهها، نیازمند تشخیص و تعیین پهنههای سیلخیز است. مدلسازی عاملمبنا[1](ABM) رویکردی برای ارائه سیستمهای شبیهسازی و انتزاعی بهمنظور کشف و بررسی الگوهای برآمده از عوارض مرتبط به محیطهای مورد مطالعه میباشد. بهعبارت دیگر، مدلسازی عاملمبنا بهعنوان رویکردی نوین برای توسعه ابزارهای شبیهسازی در پدیدههای پیچیدهی حوزههای مختلف از جمله بلایای طبیعی، مطالعات بیولوژیکی و شرایط امداد و نجات سیل میتواند مورد استفاده قرار گیرد. در این تحقیق، از دو رویکرد استنتاج فازی با درنظر گرفتن پارامترهای مؤثر بر وقوع سیلاب و با بهرهگیری از دادههای حاصل از سنجش از دور و مدلسازی عاملمبنا برای تهیه نقشه خطر سیل بهعنوان راهکارهای بازدارنده در جلوگیری از مخاطرات سیل در راستای مدیریت و تصمیمگیری قبل از وقوع سیل استفاده شده است. در نهایت نیز به مقایسه این دو رویکرد و بررسی کارکردهای آنها پرداخته شده است. نتایج نشاندهنده پیچیدگی و دقت بیشتر روشهای چند معیارهای مانند استنتاج فازی میباشد. در حالیکه روشهای مبتنی بر هوش مصنوعی و مدلسازی عاملمبنا سریعتر بوده و پیچیدگی این روش بهدلیل استفاده از برنامههای نسبتاً آماده کمتر و در عین حال، دقت این روش نیز در مقایسه با روش منطق فازی کمتر است.
[1]- Agent Based Modelling
کلیدواژهها
عنوان مقاله [English]
Offering flood prevention solutions using remote sensing and approaches integrating fuzzy logic and agent-based modeling
نویسندگان [English]
- Zahra Rezaee 1
- Mohammad Hasan Vahidnia 2
1 PhD Student in Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده [English]
Extended abstract
Introduction
Population growth, urbanization and land use change in recent decades have made floods one of the most devastating natural disasters in the world. Therefore, understanding this phenomenon, its effects and methods used to deal with it is considered to be among the most important issues crisis management planners and policymakers in urban and rural areas should pay attention to. Iran faces many natural disasters among which flood is one of the most serious ones. Monitoring and controlling accidents, assessing damages and providing relief are among the main concerns of government and crisis management experts. Continuous monitoring before the occurrence, and accurate assessment during and after the event can decrease damages to human and natural resources. Preventing flood related hazards, organizing and managing flood water in channels and ultimately improving channels require identifying and determining flood zones.
Materials & Methods
Agent-based modeling (ABM) provides simulation and abstract systems used to identify patterns of land forms in the study area. As a new approach, agent-based modeling is used to develop simulation tools for complex phenomena in various fields such as natural disasters, biological studies and relief provision in flood occurrences. In fact, agent-based modeling (ABM) has been increasingly used to confront the risk of flood and its challenges in recent years. The present study applies fuzzy inference approach (using parameters affecting the occurrence of flood and remote sensing data) and agent-based modeling to prepare a flood risk map and provide a deterrent solution for flood risk management and decision making before the occurrence. In the fuzzy inference system, various maps are prepared showing parameters affecting the occurrence of floods such as slope, soil type and rivers. Then, Fuzzy Overlay model is used to define the flood risk zones and overlay the fuzzy parameters. The present study applies fuzzy gamma operator with a coefficient of 0.8 in the final fuzzy overlay calculation.
Results & Discussion
Comparing the results obtained from overlaid maps reveals that most flood plains are located in areas covered with Affisols (clay-rich soil) and low-lying arable lands and orchards. In agent-based modeling, GIS plugin of NetLogo was used to investigate the flood phenomenon based on the digital elevation model of the area. In this model, raindrop cycle was simulated in the DEM raster layer of Gilan. DEM layer can be used to calculate the slope (vertical angle) and slope direction (horizontal angle) of the ground surface. Simulated images shows the movement and accumulation of agents along the rivers and their surroundings and in low altitude areas. Analysis confirms the risk of floods in rivers and low-lying areas. Finally, georeferenced images of points in risk of possible flood (agents in the slopes of the study area), land use map and soil cover map can be overlaid to evaluate the obtained results. Results indicate that the highest number of agents (white markings on the map) are located in agricultural land use covered with Affisols while a relatively moderate number of agents are located in agricultural lands covered with Inceptisols. As previously mentioned, these agents simulate the amount of runoff accumulation due to atmospheric precipitation. Results indicate that precipitation models simulated using artificial intelligence lead to almost the same result Fuzzy analysis method shows (regarding the prediction of flood occurrence).
Conclusion
Finally, these two approaches are compared and their functions are examined. It should be noted that multi-criteria methods such as fuzzy inference approach has a higher level of complexity and accuracy, while methods based on artificial intelligence and agent-based modeling are faster. On the other hand, agent-based modelling method use relatively ready programs and thus has a lower level of complexity. The level of accuracy in this method is also lower than the fuzzy logic method.
کلیدواژهها [English]
- Flood risk zoning map
- Fuzzy inference
- Agent-based modeling
- Gilan Province