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ارزیابی مدلهای فراابتکاری در تحلیل سطح آب زیرزمینی دشت دلفان لرستان

نوع مقاله : مقاله پژوهشی

نویسندگان

ابراهیم نوحانی 1
حمیدرضا باباعلی 2
رضا دهقانی 3

1 استادیارگروه عمران، مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

2 دانشیار، گروه مهندسی عمران، دانشگاه ازاد واحد خرم اباد، خرم آباد، ایران

3 دکتری علوم و مهندسی آب، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان لرستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، خرم آباد، ایران.

https://doi.org/10.22077/jaaq.2025.8834.1096
چکیده
آب زیرزمینی اغلب یکی از منابع طبیعی مهم تأمین آب شیرین، به ویژه در مناطق خشک و نیمه‌خشک است و از اهمیت بالایی برخوردار است. این مطالعه یک تکنیک جدید و بسیار دقیق برای پیش‌بینی سطح آب زیرزمینی ارائه می‌دهد. در این تحقیق، از مدل هوشمند ترکیبی مبتنی بر رویکرد مدل شبکه عصبی مصنوعی برای پیش‌بینی سطح آب زیرزمینی توسعه داده‌شده است. بدین منظور در این پژوهش از سه الگوریتم بهینه‌سازی شامل موجک، نهنگ و ازدحام ذرات برای مدلسازی سطح آب زیرزمینی بکار برده شد. جهت مدلسازی از آمار و اطلاعات چاههای پیزومتری شهرستان دلفان واقع در استان لرستان بعنوان مطالعه موردی طی 4 سناریو ترکیبی از پارامترهای ورودی در سالهای 1392 تا 1402 استفاده شد. به‌منظور ارزیابی عملکرد مدلها از معیارهای ارزیابی ضریب همبستگی، ریشه میانگین مربعات خطا، میانگین قدر مطلق خطا و ضریب نش ساتکلیف استفاده شد. همچنین جهت تحلیل نتایج مدلها از نمودار سری زمانی و تیلور استفاده شد. نتایج نشان داد سناریوهای ترکیبی در مدل‌های موردبررسی باعث بهبود عملکرد مدل می‌شود. همچنین نتایج حاصل از معیار ارزیابی نشان داد مدل شبکه عصبی مصنوعی-موجک (ضریب همبستگی962-951/0 ، ریشه میانگین مربعات خطا (m)224-436/0 ، میانگین قدر مطلق خطا (m) 215-375/0 و ضریب نش ساتکلیف 960-970/0) نسبت به سایر مدلهای مورد بررسی از عملکرد بهتری برخوردار است. درمجموع نتایج نشان داد استفاده از مدل‌های هوشمند مبتنی بر رویکرد شبکه عصبی مصنوعی می‌تواند گامی مؤثر در جلوگیری از کاهش سطح آب زیرزمینی و پدیده فرونشست باشد.

کلیدواژه‌ها

دلفان
سطح آب زیرزمینی
شبکه عصبی مصنوعی
مدلسازی
موضوعات

عنوان مقاله English

Evaluation of metaheuristic models in groundwater level analysis of Delfan Plain, Lorestan

نویسندگان English

Ebrahim Nohani 1
Hamidreza Babaali 2
Reza Dehghani 3
1 Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran.
2 Associate Professor, Department of Civil Engineering, Azad University, Khorramabad Branch, Khorramabad, Iran
3 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramab
چکیده English

Groundwater plays a crucial role in supplying freshwater, especially in arid and semi-arid regions. This study presents a novel and accurate approach for predicting groundwater level. The metaheuristic algorithms, namely, whale optimization algorithm (WOA), particle swarm optimization (PSO), and wavelet optimization algorithm (WV) are used to train the artificial neural network (ANN) model for predicting groundwater level. In this study, a dataset consisting of 20 wells and Piezometric observations from Delfan Plain, Lorestan province, is used for training and testing of the proposed models. The performance of the models is evaluated using statistical metrics, including coefficient of correlation ®, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The results indicate that the hybrid models outperform the traditional models in terms of prediction accuracy. The ANN-WV model exhibits the best performance among the developed models, with R = 0.976, RMSE = 0.341, MAE = 0.267, and NSE = 0.956. The results of this study demonstrate the potential of metaheuristic optimization algorithms in improving the accuracy of groundwater level prediction models.

کلیدواژه‌ها English

Groundwater level
Artificial neural network
Metaheuristic optimization
Delfan plain
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