استفاده تلفیقی از تصاویر پردازش شده با موجک و سیستم استنتاج فازی عصبی جهت برآورد پارامتر EC رودخانه کارون

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دکتری هیدرولوژی و منابع آب ، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز

2 استاد گروه هیدرولوژی و منابع آب ، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز.

3 دانشیار گروه هیدرولوژی و منابع آب ، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز.

10.22055/jise.2020.31187.1879

چکیده

امروزه پایش و پردازش اطلاعات کیفی رودخانه با استفاده از روش‌های آزمایشگاهی با صرف وقت و هزینه زیاد همراه است. از این رو استفاده از روش‌های نوین برای کاهش این هزینه‌ها می‌تواند کمک شایانی در مدیریت کیفی رودخانه‌ها داشته باشد. استفاده ترکیبی از مدل‌های هوش مصنوعی و سنجش از دور از جمله روش‌های کارآمد برای رسیدن به این هدف است. در این تحقیق به­منظور برآورد EC رودخانه کارون با استفاده از مدل سیستم استنتاج فازی عصبی (ANFIS)، در زمان گذر ماهواره لندست 8، نمونه‌برداری دستی از 66 نقطه از سطح رودخانه برای مدت 12 ماه (دی ماه 1394 تا آذر ماه 1395) انجام شد. هشت ماه اول نمونه‌برداری برای واسنجی و چهار ماه انتهایی برای صحت­سنجی مورد استفاده قرار گرفت. مقادیر بازتابی تصاویر لندست 8 به­عنوان ورودی و EC نقاط برداشت­ شده به­ عنوان خروجی مدل ANFIS در نظر گرفته شد. در ادامه به­منظور افزایش دقت مدل ANFIS و کاهش خطا تصاویر از روش تبدیل موجک و مکان­مند کردن داده‌ها استفاده گردید. نتایج نشان داد که استفاده ترکیبی از تصاویر ماهواره‌ای و مدل ANFIS از  عملکرد نسبتاً خوبی برخوردار است و با روش مکان­مند کردن یعنی اضافه کردن خصوصیات مکانی نقاط برداشت به­عنوان ورودی مدل ANFIS دقت کار تا مقدار قابل توجهی افزایش می‌یابد. هم­چنین نتایج نشان داد که استفاده از تبدیل موجک برای کاهش نویز تصاویر و بهبود عملکرد مدل می‌تواند علاوه بر کاهش خطا، ضریب تعیین را از 85 درصد تا بالای 89 درصد افزایش دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Combined use of Processed Images by Wavelet and Neural Fuzzy İnference System to Estimate EC Parameter of the Karun River

نویسندگان [English]

  • Amir Pourhaghi 1
  • Alimohammad Akhondali 2
  • heidar zarei 3
1 Department of Hydrology and Water Resources, Faculty of Water Science, Shahid Chamran University of Ahvaz, Iran,
2 Professor, Faculty Member of Hydrology and Water Resource Engineering Department of Shahid Chamran University of Ahvaz, Iran.
3 Associate Professor, Faculty Member of Hydrology and Water Resource Engineering Department of Shahid Chamran University of Ahvaz, Iran.
چکیده [English]

Nowadays, monitoring of river quality information is one of the most important issues in water resources engineering because of the direct relationship of water quality with environmental health and quality of life. Today, traditional methods of river monitoring are receiving less attention due to the fact that they are costly and time-consuming for the researcher. Instead, the recent, low-cost methods are favorable to many researchers in this filed. Different methods have always been considered for river monitoring, but the application of spectral indicators and remote sensing technologies to control and monitor the water quality of rivers and reservoirs is very cost-effective and could be a good alternative to traditional methods. Since it is time-saving and less costly, it would be a good indicator for the whole region and a good alternative to manual methods (Bonansea et al., 2015).
Although satellite imagery has been widely used in estimating water quality indices (Onderka and Pekárová, 2008), the complexity of hydrological systems and the presence of noise in images can increase the calculation error. Wavelet transform and intelligent models are among the most efficient methods that can significantly increase computation accuracy by filtering and noise reduction. Good research has been done on the use of wavelet transform in image processing (Graps, 1995) and fuzzy inference system to estimate water quality parameters (Solgi et al., 2017). In this study, using wavelet transform, Landsat 8 images were processed, then the processed images were considered as inputs of ANFIS model.

کلیدواژه‌ها [English]

  • ANFIS
  • Intelligent models
  • Salinity
  • Wavelet Transforms
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