تخمین کدورت و غلظت کلروفیل آ در دریای خزر از طریق آنالیز چندزمانه تصاویر ماهواره‌ای مادیس و شبکه‌های عصبی موجکی

نوع مقاله : علمی - پژوهشی

نویسندگان

دانشگاه خواجه‌نصیرالدین طوسی

چکیده

با توجه به وسعت جهانی منابع آب، اندازه‌گیری‌های زمینی از پارامترهای کیفی امکان‌پذیر نیست، همچنین نمونه‌برداری سنتی از آب و آنالیزهای آزمایشگاهی بسیار پرهزینه و زمان‌بر است. در مطالعات صورت گرفته، برآورد کدورت و غلظت کلروفیل آ به‌عنوان مهم‌ترین پارامترهای کیفی آب با استفاده از شبکه‌های عصبی مصنوعی با دقت مناسب توسط پژوهشگران انجام‌شده است. با توجه به مشکلاتی که در تهیه تعداد بالایی از داده‌های آموزشی در محیط‌های آبی وجود دارد استفاده از شبکه‌های ترکیبی مقاوم‌تری نظیر شبکه عصبی موجکی پیشنهادشده است. در این تحقیق انواع مختلفی از توابع موجک به‌عنوان تابع محرک شبکه مورداستفاده قرار گرفت و بهترین شبکه به‌منظور برآورد غلظت کلروفیل آ و کدورت به ترتیب شبکه‌های عصبی موجکی با تابع محرک مورلت و کلاه مکزیکی به دست آمد، داده‌های مورداستفاده محصول بازتاب اقیانوسی سنجنده مادیس است، به دلیل به‌کارگیری تصاویر چند زمانه نرمال‌سازی رادیومتریک داده‌ها انجام شد و نتایج نسبت به زمانی که از تصاویر نرمال نشده استفاده‌شده است، به‌صورت قابل‌توجهی بهبود یافت. در حالت چندزمانه علاوه بر افزایش تعداد داده‌های آموزشی، قابلیت تعمیم‌پذیری شبکه به سایر روزهایی که در آن تعداد داده میدانی کافی موجود نیست، فراهم‌شده است و دقت شبکه در این حالت در مقایسه باحالت تک زمانه افزایش یافت، درنهایت RMSE برای بهترین مدل به‌منظور برآورد کدورت و غلظت کلروفیل به ترتیب 047/0 و 071/0 به دست آمد که در مقایسه با دقت اندازه‌گیری میدانی 1/0، قابل‌قبول بوده و می‌تواند جایگزین مناسبی برای اندازه‌گیری‌های میدانی باشد.

عنوان مقاله [English]

Estimation of turbidity and chlorophyll a concentration in the Caspian Sea through time series analysis of satellite images and wavelet neural networks

نویسندگان [English]

  • Melika Haghparast
  • Mehdi Mokhtarzade
K.N.Toosi University
چکیده [English]

Due to the global scope of water resources, ground measurements of the quality parameters are not feasible, as well as traditional sampling of water and laboratory analysis is very costly and time-consuming. In studies, estimation of turbidity and chlorophyll a concentrations as the most important water quality parameters using artificial neural networks have been done by researchers. Considering the difficulties in providing a high number of training data in aquatic environments, the use of more robust hybrid networks such as the wavelet neural network is suggested. In this research, various types of wavelet functions were used as a network activation function, and the best network was used to estimate chlorophyll a and turbidity respectively, wavelet neural networks with a Morelt and a Mexican hat activation function, the data used for the reflection of the ocean reflectance of the modis sensor, Due to the use of multi-time images, the radiometric normalization of data was done and the results were significantly improved compared to the time when the non-normalized images were used. in addition to increasing the number of training data, the network generalization capability is provided to other days, and the accuracy of the network in this case increased compared to the one-day condition. the RMSE for the best model to estimate turbidity And chlorophyll a concentration was 0.047 and 0.071, respectively, which is acceptable in comparison with field accuracy of 0.1, and can be a alternative method for field measurements.

کلیدواژه‌ها [English]

  • remote sensing
  • Water Quality Parameters
  • Chlorophyll_a Concentration
  • Wavelet Neural Network

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