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ارزیابی کارایی روش‌های شبکة عصبی مصنوعی و رگرسیون چندمتغیره در بررسی افت سطح آبخوان‌های کشور (مطالعة آبخوان دشت اراک) | ||
| نشریه محیط زیست طبیعی | ||
| دوره 76، شماره 3، مهر 1402، صفحه 399-412 اصل مقاله (1.23 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jne.2023.353826.2518 | ||
| نویسندگان | ||
| سعادت حنیفیان1؛ محمد رضا خالقی* 2؛ محسن نجارچی1؛ رضا جعفرنیا1؛ جواد وروانی3 | ||
| 1گروه مهندسی آب، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران. | ||
| 2گروه کشاورزی و منابع طبیعی، دانشگاه آزاد اسلامی، واحد تربت جام، تربت جام، ایران. | ||
| 3گروه منابع طبیعی و محیط زیست، دانشکده فنی مهندسی و کشاورزی، مرکز تحقیقات علوم گیاهی، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران. | ||
| چکیده | ||
| در سال های اخیر، منابع آب زیرزمینی دشت اراک تحت تنش شدید قرار گرفته است، به طوری که در بعضی مناطق به‌علت خشک شدن چاه های بهره‌برداری، برای دسترسی به آب اقدام به افزایش عمق چاه کرده اند. در بعضی مناطق، سطح آب زیرزمینی بالاست که در آینده، زهدار شدن آن اراضی را درپی خواهد داشت. برای ساماندهی و سنجش واکنش منابع آب زیرزمینی دشت اراک در مقابل اعمال سناریوهای مختلف مدیریتی و اجرایی از مدل سازی منطقه ای استفاده شد. هدف این مطالعه، بررسی عوامل مؤثر در عمق سفره های آب زیرزمینی به‌منظور ارائة مدل منطقه ای با روش رگرسیون چند متغییره برای آبخوان دشت اراک بود. بدین‌منظور عمق متوسط سفره های آب زیر زمینی در دشت اراک، به‌عنوان متغییر وابسته و عوامل هدایت آبی تشکیلات آبخوان، ارتفاع، متوسط بارش منطقه، میزان تبخیر و فاصله از منابع آبی به‌عنوان متغیرهای مستقل در نظر گرفته شد و در محیط نرم‌افزار SPSS تجزیة رگرسیونی به‌منظور ارائة یک مدل خطی انجام گرفت. در مرحلة بعد مدل ارائه شده با بکارگیری در مکان هایی که از آمار و اطلاعات آن برای ارائة مدل استفاده نشده بود، مورد ارزیابی و کارایی آن مورد بررسی قرار گرفت و در نهایت با بکارگیری این مدل در محیط GIS نقشة عمق سفرة آب زیرزمینی برای منطقة مورد مطالعه تهیه شد. همچنین از شبکة عصبی مصنوعی ANN برای شبیه سازی عمق آب زیرزمینی استفاده گردید. عملکرد شبکة عصبی از طریق پارامترهایی چون خطای جذر میانگین مربعات (RMSE) و ضریب همبستگی بین خروجیهای حقیقی و دلخواه (R) سنجیده شد. نتایج حاصل از هر دو روش نشان داد که عوامل نوع قابلیت انتقال تشکیلات آبخوان، افت سفره، توپوگرافی (ارتفاع محل چاه در سطح حوضه آبخیز)، مقادیر بهره برداری در حداکثر شعاع عمل چاه و فاصله از منابع آب از عوامل اصلی افت آب زیرزمینی می باشند اما کارایی شبکة عصبی مصنوعی در برآورد افت آب زیرزمینی بیشتر از روش رگرسیون چندمتغیره است. | ||
| کلیدواژه‌ها | ||
| افت آب زیرزمینی؛ تشکیلات آبخوان؛ رگرسیون چندمتغیره؛ شبکة عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Evaluating the effectiveness of artificial neural network and multivariate regression methods in investigating the drawdown in the level of aquifers (case study: Arak plain aquifer) | ||
| نویسندگان [English] | ||
| Saadat Hanifian1؛ Mohammad Reza Khaleghi2؛ Mohsen Najarchi1؛ Reza Jafarnia1؛ Javad Varvani3 | ||
| 1Department of Water Engineering, Arak Branch, Islamic Azad University, Arak, Iran. | ||
| 2Department of Agriculture and Natural Resources, Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran | ||
| 3Department of Natural Resources and Environmental Sciences, Applied Plant Science Research Center, Arak Branch, Islamic Azad University, Arak, Iran. | ||
| چکیده [English] | ||
| In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, they have increased the depth of wells to access water. In some areas, the groundwater depth (GWD) is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the GWD to provide a regional model with multiple linear regression (MLR) method for Arak plain aquifer. For this purpose, the average GWD in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GWD map for the region was created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root mean square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GWD drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values at the maximum operating radius of the well, and the distance from water resources are the main factors of GWD drawdown. But the effectiveness of ANN in estimating GWD drawdown is higher than the MLR method. | ||
| کلیدواژه‌ها [English] | ||
| Aquifer formation, Artificial neural network, GWD drawdown, Multiple linear regression | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 139 تعداد دریافت فایل اصل مقاله: 163 |
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