ارزیابی و پیش‌بینی تغییرات دلتای رسوبی دهانه‌ی خورهای مکران

نوع مقاله : پژوهشی

نویسندگان

دانشکده‌ی مهندسی عمران، دانشگاه صنعتی شاهرود، شاهرود، ایران.

10.24200/j30.2025.64746.3339

چکیده

ارزیابی پارامتر‌های مؤثر در تغییرات دلتای رسوبی و پیش‌بینی تغییرات مساحت آن‌ها در مقیاس 20 ساله در سواحل مکران توسط مدل‌های یادگیری ماشین، مانند: درخت تصمیم، ماشین بردار پشتیبان، شبکه‌ی عصبی چندلایه، و آنالیز تصاویر ماهواره‌ای انجام شده است. عوامل مؤثر در تغییرات دهانه در 5 گروه، شامل 34 مؤلفه و 2100 داده در طی 20 سال بررسی شده‌اند. داده‌ها توسط آنالیز تصاویر ماهواره‌ای از پایگاه ECWMF سری era-intrim و از پایگاه‌های چابهار و جد به‌دست آمده‌اند. مدل‌های یادگیری ماشین می‌‌توانند پیش‌بینی و ارزیابی خوبی از تغییرات دلتای رسوبی دهانه‌ی خورهای مکران انجام ‌دهند. مدل درخت تصمیم، بهترین عملکرد را با خطای کمتر از 8٪ داشته است. از میان 34 مؤلفه‌ی انتخابی، مؤثرترین مشخصه‌های اثرگذار در دهانه‌های خورهای مکران در بلندمدت، داده‌هایی با مقیاس جهانی، مانند: بالاروی سطح دریا، گرمایش جهانی، و باریاستاتیک بوده‌ و سپس داده‌های هواشناسی مانند تعداد روزهای بارش در مرتبه‌ی بعدی قرار گرفته‌اند. شاخص ارتقاء یافته‌ی ویلموت و شاخص‌های MAE و MSE، مدل درخت تصمیم را به‌عنوان بهترین مدل برای پیش‌بینی تغییرات دلتای دهانه‌ی خورهای مکران معرفی کرده‌اند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation and Prediction of Sedimentary Delta Dynamics in the Makoran Tidal Inlets

نویسندگان [English]

  • Saeid Roodbari Shahmiri
  • Mehdi Adjami
  • Saeid Gharechelou
Faculty of Civil Engineering, Shahrood University of Technology, Iran.
چکیده [English]

Estuaries and tidal inlets are valuable environments for marine ecosystems and have strong socio-economic importance for coastal communities (maritime, fishing, aquaculture, tourism, recreational activities, etc.). Estuaries are connected to the sea by inlets. Various models of changes in the area of ​​the sedimentary delta of inlets with different behaviors have been identified. In this paper, an evaluation of the parameters affecting sedimentary delta changes and a prediction of changes in the sedimentary delta area of ​​mixed energy-dominated estuaries (simultaneously dominated by waves and tides) on a 20-year scale has been carried out on the Makoran coast. This assessment was carried out using machine learning models such as decision trees, support vector machines, multilayer neural networks, and satellite image analysis. Factors affecting changes in inlets have been evaluated in 5 main groups, including 34 components, 2100 data points over a period of 20 years. The data evaluated in the research were obtained by analyzing satellite images, extracting and classifying data from the European Weather Database (ECWMF) from the era-intrim data series, and data from the Chabahar and Jad field stations. The results showed that machine learning models, with the appropriate data bank and selection of appropriate features, can predict and evaluate changes in the sedimentary delta of the Makoran inlets dominated by mixed energy. Among the three models evaluated, the decision tree model provided the best performance with an error of less than 8% for these openings. Among the 34 selected components, the most influential characteristics affecting these inlets in the long term are global-scale data such as sea level rise, global warming, and barystatics. In the second order of importance of data, there are meteorological data, such as the number of rainy days per year and precipitation. The enhanced Wilmot index and the MAE and MSE indices have selected the decision tree model as the best model for predicting changes in the delta of the Makoran mixed energy inlets.

کلیدواژه‌ها [English]

  • Tidal inlets
  • mixed energy inlets
  • machine learning
  • sediment delta
  • satellite images

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