مقایسه روش‌های درون‌یابی به‌منظور بهبود پیش‌بینی سطح ایستابی آب زیرزمینی با استفاده از روش‌های یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.

10.22059/jwim.2024.372424.1145

چکیده

منابع آب زیرزمینی عامل مهمی در مدیریت و نگهداری آب است که برای آب آشامیدنی، آبیاری و سایر اهداف استفاده می­شود. پیش‌بینی سطح آب زیرزمینی برای ارزیابی کل منابع آب و تخصیص آن‌ها، کمک به پایداری آب و کاهش خشکسالی بسیار مهم است. برخی اوقات وجود موانعی مانند نامساعدبودن شرایط جوی، مسدودبودن راه­ها و یا نبود تجهیزات و افراد کافی اندازه‌گیری تا ماه­ها انجام نمی­گیرد. از طرفی داده‌های دقیق و فراوان سطح آب زیرزمینی به پیش‌بینی پیامدهای مختلف مربوط به مدیریت آب زیرزمینی و سلامت اکوسیستم کمک می­کند. با این وجود تکمیل داده‌های مفقودشده و بهبود آن‌ها به‌روش درون‌یابی کمک مؤثری در پیش‌بینی سطح ایستابی به‌روش یادگیری عمیق می­کند. در این مطالعه آبخوان آذرشهر که به‌تازگی با افت سطح آب زیر­زمینی قابل‌توجهی روبه‌رو شده است به‌صورت ماهیانه از سال 1397 تا 1400 موردبررسی قرار گرفت. هم‌چنین جهت تکمیل داده‌هایی که به هر علتی اندازه‌گیری نشده بود از روش‌های درون‌یابی کریجینگ و الگوریتم M5P استفاده شد که با تجزیه و تحلیل هر روش، روش M5P با حداقل ریشه میانگین مربع خطا 83/1 متر و ضریب همبستگی 975/0 بهترین عملکرد را داشت. از طرفی برای پیش­بینی سطح آب زیرزمینی داده‌ها به دو صورت واسنجی و صحت‌سنجی70 به 30 تقسیم‌بندی شده و از روش یادگیری عمیق (DL) بهره گرفته شد که این روش با خطای 408/1 متر و دقت 88 درصد، قابل‌قبول بوده و می­توان در پژوهش‌های آتی جهت مدیریت  بهتر منابع آبی مورداستفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comparison of interpolation methods to improve the prediction of groundwater surface level using deep learning

نویسندگان [English]

  • Erfan Abdi
  • ٍEsmaeil Asadi
  • Mohammad Ali Ghorbani
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
چکیده [English]

Groundwater resources are an important factor in managing and maintaining water that is used for drinking water, irrigation and other purposes. Groundwater level forecasting is very important for assessing total water resources and their allocation, contributing to water sustainability and drought mitigation. Sometimes, due to the presence of obstacles such as unfavorable weather conditions, blocked roads, or lack of equipment and people, measurements are not carried out for months. On the other hand, accurate and abundant groundwater level data helps to predict various consequences related to groundwater management and ecosystem health. Nevertheless, completing the missing data and improving them by interpolation method helps effectively in predicting the stability level by deep learning method. . In this study, the Azarshahr aquifer, which has recently faced a significant drop in the underground water level, was examined monthly from 1397 to 1400. Also, in order to complete the data that was not measured for any reason, kriging interpolation methods and M5P algorithm were used. By analyzing each method, the M5P method with the minimum root mean square error of 1.83 meters and correlation coefficient of 0.975 was the best. It had the function. On the other hand, in order to predict the underground water level, the data was divided into 70 and 30 calibration and accuracy measurements, and the deep learning (DL) method was used, which was acceptable with an error of 1.408 meters and an accuracy of 88%. And it can be used in future research for better management of water resources.

کلیدواژه‌ها [English]

  • Prediction
  • Interpolation
  • groundwater level
  • deep learning
  • M5P

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مدیریت آب و آبیاری
دوره 14، شماره 3
آذر 1403
صفحه 665-679

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