مدل سازی ارتباط کیفیت آب های سطحی و سنجه های سیمای سرزمین با استفاده از سیستم استنتاج عصبی-فازی (مطالعه موردی: استان مازندران)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد محیط زیست، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، نور، مازندران

2 دانشیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، نور، مازندران

3 دانشیار گروه محیط زیست، دانشکده شیلات و محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

4 استادیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، نور، مازندران

چکیده

تغییرات کیفیت آب، رویکرد مناسبی برای پایش آلودگی‌های غیر نقطه‌ای است. در مطالعه حاضر، اطلاعات کیفیت آب 81 ایستگاه آبسنجی واقع بر رودخانه‌های استان مازندران در خلال سال‌های 1391 و 1392 مورد بررسی قرار گرفت. مرز زیر حوضه‌های بالادست ایستگاه‌ها ترسیم شد و سنجه‌های سیمای سرزمین در دو سطح کلاس و سیما برای زیر حوضه‌های موجود استخراج شدند. از تحلیل مولفه‌های اصلی برای تعیین پارامترهای کیفیت آب و از رگرسیون خطی پیشرو به‌منظور تعیین سنجه‌های بهینه در توصیف تغییرات هر کدام از پارامترها استفاده شد. پنج مؤلفه اول قادر به توصیف 61/96 درصد از تغییرات کیفیت آب رودخانه‌های استان مازندران بودند. برای مدل‌سازی ارتباط میان سنجه‌های سیمای سرزمین و پارامترهای کیفیت آب از شبکه عصبی-فازی تطبیقی و رگرسیون چند متغیره خطی استفاده شد. نتایج حاکی از بود رگرسیون چند متغیره خطی توانسته است پارامترهای SAR، TDS، pH، NO3 و PO43- را با ضریب تبیین 81/0، 56/0، 73/0، 44/0 و 63/0 در مرحله آزمون پیش‌بینی نماید. این در حالی است که ضریب تبیین شبکه عصبی-فازی به‌ترتیب برابر با 82/0، 79/0، 82/0، 31/0 و 36/0 بوده است. بنابراین، شبکه عصبی-فازی در اغلب موارد کارایی بالاتری داشته و این امر ارتباط غیر خطی میان پارامترهای کیفیت آب و سنجه‌های سیمای سرزمین را نشان می‌دهد. از آنجا که کاربری‌ها و پوشش سرزمین مختلف در حوضه دارای تأثیر زیادی بر کیفیت آب خروجی، آلاینده‌های در دسترس و بار مواد محلول در رودخانه‌ها هستند، استفاده از روش این پژوهش می‌تواند به‌عنوان ابزاری تکمیلی در برنامه‌ریزی منطقه‌ای و ارزیابی اثرات محیط زیستی در برنامه‌های توسعه مطرح شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling Relationships between Surface Water Quality and Landscape Metrics Using the Adaptive Neuro-Fuzzy Inference System, A Case Study in Mazandaran Province

نویسندگان [English]

  • mohsen Mirzayi 1
  • Alireza Riyahi Bakhtiyari 2
  • Abdolrasool Salman Mahini 3
  • Mehdi Gholamali Fard 4
1 MSc Graduate Student of Environment, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran
2 Assoc. Prof. of Environment, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran
3 Assoc. Prof. of Environment, Faculty of Fisheries and Environment, Gorgan University of Natural Resources and Agriculture, Gorgan
4 Assist. Prof. of Environment, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran
چکیده [English]

 Landscape indices can be used as an approach for predicting water quality changes to monitor non-point source pollution. In the present study, the data collected over the period from 2012 to 2013 from 81 water quality stations along the rivers flowing in Mazandaran Province were analyzed. Upstream boundries were drawn and landscape metrics were extracted for each of the sub-watersheds at class and landscape levels. Principal component analysis was used to single out the relevant water quality parameters and forward linear regression was employed to determine the optimal metrics for the description of each parameter. The first five components were able to describe 96.61% of the variation in water quality in Mazandaran Province. Adaptive Neuro-fuzzy Inference System (ANFIS) and multiple linear regression were used to model the relationship between landscape metrics and water quality parameters. The results indicate that multiple regression was able to predict SAR, TDS, pH, NO3, and PO43 in the test step, with R2 values equal to 0.81, 0.56, 0.73, 0.44. and 0.63, respectively. The corresponding R2 value of ANFIS in the test step were 0.82, 0.79, 0.82, 0.31, and 0.36, respectively. Clearly, ANFIS exhibited a better performance in each case than did the linear regression model. This indicates a nonlinear relationship between the water quality parameters and landscape metrics. Since different land cover/uses have considerable impacts on both the outflow water quality and the available and dissolved pollutants in rivers, the method can be reasonably used for regional planning and environmental impact assessment in development projects in the region.

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