بهبود نتایج حاصل از مدل دراستیک با استفاده از هوش مصنوعی جهت ارزیابی آسیب پذیری آبخوان آبرفتی دشت رامهرمز

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجو/دانشگاه شهید چمران اهواز

2 دانشیار/دانشگاه شهید چمران اهواز

3 استادیار/دانشگاه شهید چمران اهواز

4 کارشناس ارشد/دانشگاه شهید چمران اهواز

چکیده

سابقه و هدف: آلودگی آب‌های زیرزمینی یک فرآیند پیچیده و پر از عدم قطعیت، در مقیاس منطقه‌ای می‌باشد. توسعه یک روش یکپارچه جهت ارزیابی آسیب‌پذیری آبخوان‌ها، می‌تواند به منظور مدیریت بهینه و حفاظت از آن‌ها کارامد باشد. دشت رامهرمز به دلیل داشتن خاک حاصلخیز و منابع آب کافی دارای زمین‌های مستعد کشاورزی است که به دلیل توسعه کشاورزی، استفاده از کودهای شیمیایی و مواد آفت‌کش‌ همواره در معرض خطر آلودگی قرار دارد. یکی از راه‌های مناسب برای جلوگیری از آلودگی آب‌های زیرزمینی، شناسایی مناطق دارای پتانسیل آلودگی می‌باشد. هدف از مطالعه حاضر، تهیه نقشه آسیب‌پذیری آبخوان آبرفتی دشت رامهرمز با استفاده از مدل دراستیک و سپس بکارگیری روش‌های هوش مصنوعی جهت بهبود نتایج حاصل از مدل دراستیک است. با توجه به اهمیت منابع آب زیرزمینی در منطقۀ مورد مطالعه که برای مقاصد مختلف از جمله کشاورزی مورد استفاده قرار می‌گیرد، مطالعۀ آسیب پذیری آبخوان و حفاظت این مناطق برای توسعه و مدیریت بهینه منابع آب ضروری به نظر می‌رسد.
مواد و روش‌ها: در این مطالعه، ارزیابی آسیب‌پذیری آبخوان آبرفتی دشت رامهرمز در ابتدا با استفاده از مدل دراستیک انجام شد و در ادامه از روش‌های هوش مصنوعی جهت بهینه‌سازی مدل استفاده گردید. مدل دراستیک شامل پارامترهای: عمق تا سطح ایستابی، تغذیه، جنس سفره، نوع خاک، توپوگرافی، مواد تشکیل دهنده منطقۀ غیراشباع و هدایت هیدرولیکی می‌باشد که در ارزیابی آسیب‌پذیری سفرۀ آب‌زیرزمینی موثر هستند. این روش بر اساس وزن‌های استاندارد پارامترهای مدل دراستیک و لایه‌های بدست آمده برای هر یک از هفت پارامتر میزان آسیب‌پذیری آبخوان را محاسبه می‌نماید. پس از آماده-سازی لایه‌ها، آسیب‌پذیری آبخوان آبرفتی دشت رامهرمز با استفاده از روش‌ دراستیک، تعیین گردید. هم‌چنین نقشۀ آسیب‌پذیری آبخوان و شاخص دراستیک برای کل منطقه محاسبه شد. به منظور ارزیابی دقت نتایج این مدل، از داده‌های غلظت نیترات موجود در آبخوان جهت صحت‌سنجی استفاده شده است. در ادامه به منظور بهبود نتایج، مدل دراستیک با روش‌های شبکه عصبی مصنوعی، منطق فازی( سوگنو و ممدانی) و سیستم استنتاج تطبیقی عصبی- فازی تلفیق شد و چهار نقشه آسیب‌پذیری با استفاده از مدل‌های مختلف هوش مصنوعی حاصل گردید.
یافته‌ها: نقشۀ آسیب‌پذیری آبخوان نسبت به آلودگی، با تقسیم‌بندی به سه محدودۀ آسیب‌پذیری کم، متوسط و زیاد تهیه و شاخص دراستیک برای کل منطقه بین 48 تا 156 محاسبه گردید. ضریب همبستگی 97/0 بین شاخص دراستیک و غلظت نیترات نشان دهنده دقت نسبتاً مناسب این روش است. نتایج نشان داد که مدل‌های هوش مصنوعی به کار گرفته شده، قابلیت بهبود نتایج مدل دراستیک اولیه را دارا می‌باشند. با مقایسه نتایج مدل‌ها می‌توان نتیجه گرفت که مدل سیستم استنتاج تطبیقی عصبی- فازی بهترین نتیجه را در بردارد.
نتیجه‌گیری: ضریب تعیین (R2) برای مدل‌های سیستم استنتاج تطبیقی عصبی- فازی، شبکه عصبی و مدل‌های فازی سوگنو و ممدانی به‌ترتیب 99/0، 94/0، 98/0 و 87/0 بدست آمد. طبق مدل نهایی، نواحی جنوب- جنوب شرقی منطقه دارای بیشترین میزان پتانسیل آلودگی هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Improve the results of the DRASTIC model using artificial intelligence methods to assess groundwater vulnerability in Ramhormoz alluvial aquifer plain

نویسندگان [English]

  • Nazanin Ghanbari 1
  • Kazem Rangzan 2
  • Mostafa Kabolizade 3
  • Poria Moradi 4
چکیده [English]

Background and objectives: Groundwater pollution is a complex and full of uncertainty process, on a regional scale. Development of an integrated method for assessing groundwater vulnerability, can be efficient in order to optimized management and protection of them. Because of fertile soil and sufficient water resources, Ramhormoz plain is suitable area for agriculture that by development of agriculture, use of chemical fertilizers and pesticide, this plain always is at risk of contamination. One of the suitable approach to prevent groundwater contamination, identify areas of potential contamination. The aim of this study is to produce vulnerability map of Ramhormoz plain alluvial aquifer using DRASTIC model, and then use artificial intelligence techniques to improve the results of the DRASTIC model. Due to the importance of groundwater resources in the study area that are used for various purposes including agriculture, Aquifer vulnerability study and protect these areas for development and management of water resources is essential.
Materials and methods: In this study, first, vulnerability evaluation of Ramhormoz alluvial aquifer plain was performed using DRASTIC model and in the following, artificial intelligence methods was used to optimize the model. DRASTIC model includes the following parameters: depth to water table, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity that are effective in groundwater vulnerability assessment. This method, based on the standard weights of DRASTIC model and obtained layers for each of the seven parameters, calculates the amount of aquifer vulnerability. After preparation of the layers, vulnerability of Ramhormoz alluvial aquifer plain was determined using drastic model. Also the groundwater vulnerability map and DRASTIC index was calculated for the entire area. In order to evaluation of accuracy of the obtained results from the model, nitrate concentration data existing in groundwater have been used for verification. Following In order to improve results, DRASTIC model was integrated by artificial neural networks, fuzzy logic (Sugeno and Mamdani) and Adaptive Neuro-Fuzzy Inference System methods and four vulnerability maps was obtained using different models of artificial intelligence.
Results: the groundwater vulnerability map toward the contamination was prepared by the division into three vulnerability ranges including low, medium and high and DRASTIC index was calculated for the entire area between 48 and 156. Correlation coefficient 0.97 between DRASTIC index and nitrate concentration reflects the relatively good accuracy of this method. Also, the results of the implementation of these models showed that the used artificial intelligence models have the ability to improve the primary DRASTIC model results. By comparing the results of the models can be concluded that the Adaptive Neuro-Fuzzy Inference System model has the best result.
Conclusion: The determination coefficient, R2, for the Adaptive Neuro-Fuzzy Inference System, neural networks and Mamdani fuzzy and Sugeno fuzzy models, is 0.99, 0.94, 0.98 and 0.87 respectively. According to the final model, South- Southeast regions have the highest potential for contamination.

کلیدواژه‌ها [English]

  • groundwater vulnerability
  • DRASTIC model
  • geographic information systems
  • Artificial intelligence
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