پیش بینی نوسانات تراز آب زیرزمینی دشت سنقر با استفاده از روشهای یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی عمران، گروه مهندسی عمران و مرکز تحقیقات مدل سازی و بهینه سازی در علوم و مهندسی واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، تهران، ایران.

2 دانشیار، گروه مهندسی عمران و مرکز تحقیقات مدل سازی و بهینه سازی در علوم و مهندسی واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، تهران، ایران.

3 استادیار، گروه مهندسی عمران و مرکز تحقیقات مدل سازی و بهینه سازی در علوم و مهندسی واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، تهران، ایران.

4 دانشیار، گروه مهندسی عمران، واحد اراک، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران.

چکیده

مدلهای عددی بر اساس آمار و اطلاعات گسترده و بر اساس نقشه ها و اندازه گیری های متنوع زمینی مانند آزمایشات پمپاژ، ژئوفیزیک، نقشه های خاک و کاربری اراضی، داده های توپوگرافی و شیب، شرایط مرزی مختلف و بهره گیری از معادلات پیچیده قادر به تخمین تراز آب زیرزمینی در هر منطقه ای هستند. در تحقیق حاضر ابتدا با استفاده از آمار و اطلاعات و نقشه های موجود نوسانات تراز آب زیرزمینی دشت سنقر توسط مدل GMS شبیه سازی شد و دقت مدل در دو مرحله واسنجی و صحت سنجی مورد ارزیابی قرار گرفت. سپس به دلیل نیاز به حجم داده بسیار کمتر در روشهای یادگیری ماشین، روش‌های هیبرید GWO-ANN و PSO-ANN و مدل‌های LSTM وSAELM مورد استفاده قرار گرفت. نتایج نشان داد خروجی مدل SAELM دارای بهترین برازش با داده‌های مشاهداتی با ضریب همبستگی برابر با 97/0 بود، همچنین دارای بهترین و نزدیک‌ترین پراکندگی نقاط در اطراف خط 45 درجه بود و از این نظر دقیق‌ترین مدل محسوب می‌شود. لذا برای پیش بینی تراز آب زیرزمینی در کل دشت بجای استفاده از مدل پیچیده GMS با حجم داده های بسیار زیاد و همچنین فرآیند واسنجی و صحت سنجی بسیار وقت گیر در آن، می توان با اطمینان از مدل SAELM استفاده کرد. این رویکرد کمک زیادی به محققین بخش آب زیرزمینی می کند تا بدون استفاده از مدلهای عددی با ساختار پیچیده و وقت گیر با استفاده از هوش مصنوعی با دقت بالا تغییرات تراز آب زیرزمینی را در سالهای خشک و تر پیش بینی نمایند.

کلیدواژه‌ها

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