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پیشبینی تابآوری آبخیز دشت مرودشت با استفاده از یک چارچوب ترکیبی مبتنی بر WoE | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 39، شماره 2 - شماره پیاپی 151، تیر 1405، صفحه 116-137 اصل مقاله (1.22 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2025.371489.1647 | ||
| نویسندگان | ||
| سعید علیزاده1؛ رضا قضاوی* 2؛ ابراهیم امیدوار3 | ||
| 1دانشجوی دکتری علوم و مهندسی آبخیزداری، دانشگاه کاشان | ||
| 2استاد دانشکده منابعطبیعی و علوم زمین دانشگاه کاشان | ||
| 3دانشیار دانشکده منابع طبیعی و علوم زمین دانشگاه کاشان | ||
| چکیده | ||
| مقدمه و هدف در قرن پیشرو با تشدید خشکسالیها و افزایش فعالیتهای انسانی بر منابع آبهای زیرزمینی، ارزیابی تابآوری سامانههای آبخوان بهعنوان یک شاخص مهم پایداری منابع آب، اهمیت ویژهای دارد. ازاینرو، برای سنجش توانایی آبخوان در بازگشت به وضعیت مطلوب پس از فشارهای آبشناختی، از شاخص کمّی (CRS) بهعنوان معیار، استفاده میشود. دشت مرودشت با 148 هزار هکتار زمینهای زارعی آبی، 22 هزار هکتار زمینهای دیم، قطب کشاورزی استان فارس است. اما بهدلیل برداشت بیرویه از منابع آب زیرزمینی چالشهای مختلفی مانند افت سطح آب زیرزمینی، فرونشست و شکافهای پرشماری رخداده است که خطر فرونشست در نواحی باستانی تختجمشید و نقشرستم نیز نمایان است. ازاینرو، این پژوهش با رویکرد جامع تلفیق مدل وزندهی شواهد (WoE)، روشهای انتخاب ویژگی پیشرفته و روشهای اعتبارسنجی و با هدف پیشبینی تابآوری آبهای زیرزمینی در آبخیز دشت مرودشت اجرا شد و نقشه کاربردی و قابل اطمینان توزیع مکانی تابآوری آبهای زیرزمینی این دشت، برای تصمیمگیریهای مدیریتی تولید شد. مواد و روشها در این پژوهش، ابتدا ۲۱ متغیر محیطی، آبزمینشناختی و اقلیمی بهعنوان عاملهای تعیینکنندة وضعیت آبهای زیرزمینی شناسایی و بهشکل لایههای شبکهای تهیه شدند. سپس، با بهکارگیری یک رویکرد چندمرحلهای ویژگیها مبتنی بر پراکنش، همبستگی، همخطی و اطلاعات متقابل، انتخاب شدند. در این پژوهش، 10 عامل از مؤثرترین عاملها (تراکم کرنل چاههای بهرهبرداری، تراکم آبراهه، فاصله از چاههای کشاورزی، فاصله از گسل، انحنای سطح، فاصله از چاههای صنعتی، میانگین افت، ضخامت آبخوان، فاصله از آبراهه اصلی، تراکم چاههای بهره برداری) برای مدلسازی نهایی انتخاب شدند. روابط میان شاخص CRS و این عاملها با روش ترکیب شواهد و محاسبه وزنهای WoE تعیین شد. با جمع وزنهایWoE ، نقشه نهایی تابآوری تولید و سپس بر اساس روش طبقهبندی چندکی به پنج طبقه تقسیم شد. اعتبارسنجی مدل با 3 روش اعتبارسنجی متقاطع طبقهبندیشده، روش بوتاسترپ (1000 تکرار) و تحلیل واسنجی، انجام شد. نتایج و بحث یافتههای اعتبارسنجی مدل نشان داد که عملکرد مدل در پیشبینی شاخص تابآوری آبهای زیرزمینی (CRS) بسیارخوب (920/0= AUC) و پایداری آماری آن قابل اطمینان است. در آبخیز مطالعهشده تابآوری آب زیرزمینی با طبقات مختلف و دقت مناسب، شناسایی شد. نقشه نهایی بر اساس روش طبقهبندی چندکی به پنج طبقه بسیارکم (0/223 - 0)، کم (0/367 – 0/223)، متوسط (0/503 – 0/367)، زیاد (0/669 – 0/503)، خیلیزیاد (1 – 0/669)، تقسیم شد. این طبقات منعکسکننده تغییرپذیری ذاتی دادهها بودند. زیرا بر اساس توزیع تجربی اندازههای شاخص بهنجارشده، که از ترکیب وزنهای WoE عاملهای مؤثر محاسبه شدند، هر طبقه تقریباً شامل 20% از سطح مطالعهشده بود. در این پژوهش، اندازههای کم شاخص تابآوری در بیشتر مناطق (میانگین = 0/048) بیانگر تابآوری بسیارکم سامانة آبخوان در برابر فشارهای آبشناختی بود. نتیجهگیری و پیشنهادها بر اساس نتایج این پژوهش میتوان مناطق بحرانی با کمترین تابآوری را شناخت. از اینرو، پیشنهاد میشود بر پایة نتایج بهدست آمده مناطقی که در طبقه بسیار کم تابآوری هستند باید در اولویت اول برنامهریزیهای مدیریتی مانند کاهش برداشتهای غیرمجاز، اجرای طرحهای تغذیه مصنوعی، ایجاد مناطق حفاظتی و نظارتی فنی- حقوقی باشند و در آنها نظارت شدید انجام شود. افزون بر این، پیشنهاد میشود از این چارچوب مدلسازی بهعنوان یک مبنای علمی قابل تعمیم برای ارزیابی تابآوری آبهای زیرزمینی در دیگر دشتهای بحرانی کشور استفاده شود. | ||
| کلیدواژهها | ||
| آب زیرزمینی؛ بوت استرپ؛ شاخص تابآوری؛ مدلسازی؛ مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Forecasting the Rresilience of the Marvdasht Plain Watershed Using a Combined WoE-Based Framework | ||
| نویسندگان [English] | ||
| Saeed Alizadeh1؛ Reza Ghazavi2؛ Ebrahim Omidvar3 | ||
| 1Ph.D. Candidate in Watershed Sciences and Engineering, Department of Nature Engineering, University of Kashan | ||
| 2Professor in Watershed Sciences and Engineering, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan | ||
| 3Associate Professor in Watershed Sciences and Engineering, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan | ||
| چکیده [English] | ||
| Introduction and Goal In the coming century, with the intensification of droughts and increasing human activities on groundwater resources, assessing the resilience of aquifer system as an important indicator of water resource sustainability is of particular importance. Therefore, to measure the ability of an aquifer to return to its desired state after hydrological pressures, a quantitative index (CRS) was used as a criterion. The Marvdasht Plain, with 148,000 ha of irrigated farmland and 22,000 hectares of rainfed farmland, is the agricultural hub of Fars Province. However, due to excessive extraction of groundwater resources, various challenges have occurred, such as falling groundwater levels, subsidence, and numerous cracks, and the risk of subsidence is also evident in the ancient areas of Persepolis and Naqsh-e Rostam. Therefore, this study was conducted with a comprehensive approach combining the Weight of Evidence (WoE) model, advanced feature selection techniques, and validation methods, with the aim of predicting groundwater resilience in the Marvdasht plain watershed, and a practical and reliable map of the spatial distribution of groundwater resilience in this plain was produced for management decisions. Materials and Methods In this study, 21 environmental, hydrogeological, and climatic variables were initially identified as factors determining groundwater status and prepared in the form of raster layers. Then, using a multi-step approach, features based on dispersion, correlation, collinearity, and mutual information were selected. In this study, 10 of the most effective factors (including kernel density of exploitation wells, stream density, distance from agricultural wells, distance from fault, surface curvature, distance from industrial wells, average drop, aquifer thickness, distance from main stream, production well density) were selected for final modeling. The relationships between the CRS index and these factors were determined using the evidence synthesis method and calculating WoE weights. By summing the WoE weights, the final resilience map was generated and then classified into five classes based on the quantile classification method. Model validation was performed using three methods: random cross-validation, the bootstrap method (1,000 replicates), and calibration analysis. Results and Discussion The model validation findings showed that the model's performance in predicting the groundwater resilience index (CRS) was very good (AUC = 0.920) and its statistical stability was reliable. In the studied watershed, groundwater resilience was identified with different classes and appropriate accuracy. The final map, was classified into five classes based on the quantile classification method: very low (0 - 0.223), low (0.223 - 0.367), medium (0.367 - 0.503), high (0.503 - 0.669), and very high (0.669 - 1.000). These classes reflected the inherent variability of the data. Because based on the empirical distribution of normalized index sizes, which were calculated from the combination of WoE weights of the effective factors, each class comprised approximately 20% of the studied area. In this study, low resilience index values in most of the area (mean = 0.48) indicated very low resilience of the aquifer system to hydrological pressures. Consequently, zones identified with the lowest resilience should be prioritized for critical management interventions, such as reducing groundwater withdrawals, implementing artificial recharge projects, and establishing intensive monitoring networks. Conclusion and Suggestions Based on the results of this research, it is possible to identify the critical areas with the lowest resilience. Therefore, it is recommended that, based on the results obtained, areas that are in the very low resilience category should be given top priority in management planning, such as reducing unauthorized withdrawals, implementing artificial recharge schemes, establishing technical-legal protection and monitoring areas, and that strict monitoring be carried out in them. Also, it is suggested that this modeling framework be used as a generalizable scientific basis for assessing groundwater resilience in other critical plains in the country. | ||
| کلیدواژهها [English] | ||
| Bootstrap, groundwater, modeling, resilience index, water resources management | ||
| مراجع | ||
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