استفاده از خوشه‌بندی مکانی برای پیش‌بینی پارامترهای کیفی آب زیرزمینی با مدل انفیس

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استاد، گروه مهندسی عمران-آب، دانشکده مهندسی عمران، دانشگاه تبریز

2 دانشجوی دکترای عمران- سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه تبریز

چکیده

مدل‌سازی پارامترهای کیفی آب‌های زیرزمینی بهعنوان یکی از منابع تأمین آب شهری، کشاورزی و صنعتی بسیار حائز اهمیت است. همراه با توسعه استفاده از روش‌های نوین مدل‌سازی در سال‌های اخیر، استفاده از ابزارهای پیش‌پردازش زمانی و مکانی داده‌ها در بالا بردن دقت این روش‌ها اهمیت زیادی یافته است. در پژوهش حاضر از ابزارهای زمین آمار یا کوکریجینگ و نقشه‌های خود سازمانده به‌ترتیب به‌عنوان تخمین‌گر مکانی و خوشه‌بندی در ترکیب با مدل شبکه عصبی- فازی انفیس برای پیش‌بینی زمانی پارامترهای کیفی هدایت الکتریکی و مقدار کل جامدات محلول در آب زیرزمینی دشت اردبیل استفاده شد. همچنین در این مطالعه، اثر خوشه‌بندی مکانی در پیش‌بینی زمانی هدایت الکتریکی و مقدار کل جامدات محلول بررسی شد. نتایج نشان داد که خوشه‌بندی مکانی با انتخاب ورودی‌های مؤثر، روش مناسبی در جهت بهبود پیش‌بینی پارامترهای کیفی آب زیرزمینی با استفاده از مدل انفیس است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Using Spatial Clustering in Forecasting Groundwater Quality Parameters by ANFIS

نویسندگان [English]

  • MohammadTaghi Alami 1
  • Vahid Nourani 1
  • Farnaz Daneshvar Vousoughi 2
1 Prof. of Civil and Water Engineering, Department of Civil Engineering, University of Tabriz
2 PhD Student of Civil-hydraulic Structures, Department of Civil Engineering, University of Tabriz
چکیده [English]

Groundwater is a major source of water supply for domestic, agricultural, and industrial uses; hence, its quality modeling is an important task in hydro-environmental studies. While many data-based models have been developed for this purpose, the performance of such data-based models can be drastically enhanced if they are based on temporal and spatial pre-processing. In this study, geostatistics tools (e.g., Co-Kriging), as spatial estimators, and self-organizing map (SOM), as a clustering technique, were employed in conjunction with Adaptive Neuro-Fuzzy Inference System (ANFIS) for the temporal forecasting of such quality parameters as electrical conductivity (EC) and total dissolved solids (TDS) of the groundwater in Ardabil Plain. Using the results thus obtained, the impact of spatial data clustering was also investigated on the same parameters. The results showed that, if propoer input data are selected, the proposed spatial clustering technique is capable of imporving groundwater quality forecasts made by ANFIS.

کلیدواژه‌ها [English]

  • Geostatistics
  • adaptive neuro-fuzzy inference system
  • Groundwater Quality Parameters
  • clustering method
  • Ardabil Plain
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