تحقیقات منابع آب ایران

تحقیقات منابع آب ایران

مقایسه عملکرد مدل‌های یادگیری عمیق با شبکه عصبی چند‌جمله ای و مدل HEC-HMS در پیش‎ بینی رواناب روزانه

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی کارشناسی ارشد، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، تهران، ایران
2 استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، تهران، ایران.
3 استاد مرکز مطالعات پیشرفته خاورمیانه و بخش مهندسی منابع آب، دانشگاه لوند، لوند، سوئد.
چکیده
تخمین رواناب ناشی از بارش مازاد حوضه آبخیز می‌­تواند کمک شایانی به طراحی دقیق سازه­‌های آبی، مدیریت جامع حوضه‌­های آبخیز و مدیریت سیلاب نماید. لذا در این پژوهش سعی شده است قدرت برآورد روش‌­های یادگیری عمیق در مقایسه با شبکه عصبی چند جمله‌ای و مدل HEC-HMS در حوضه­‌های آبخیز بار اریه، کسیلیان و لتیان مورد ارزیابی قرار گیرد. برای این منظور رواناب روزانه با استفاده از مدل LSTM شبیه‌­سازی و نتایج آن با نتایج مدل­های MLP به عنوان رایج­‌ترین مدل هوش مصنوعی، مدل GMDH به عنوان یکی از قوی­‌ترین شبکه­‌های عصبی مصنوعی و مدل HEC-HMS به عنوان یک مدل فیزیک‌­پایه مقایسه شد. نتایج پژوهش نشان داد ضریب R2 در مدل‌­های مختلف بازه‌ای بین 0/8715 تا 0/9864، ضریب RMSE بازه‌ای از 0/086 تا 2/2165 و ضریب NRMSE بازه­ای بین 18/88 تا 65/96 را در حوضه‌­های آبخیز مختلف به خود اختصاص داده است. نتایج حاکی از عملکرد متوسط مدل MLP با متوسط NRMSE معادل 51/17 درصد، عملکرد مناسب مدل GMDH با متوسط NRMSE معادل 44/6 درصد و عملکرد بسیار خوب مدل LSTM با متوسط NRMSE معادل 26/8 درصد است. با توجه به هزینه محاسباتی بالای LSTM در مقایسه با مدل GMDH می‌­توان توصیه کرد که اگر دقت و صحت خیلی بالا از طرف کاربر مورد انتظار نیست از مدل GMDH استفاده شود و در صورت نیاز به صحت بالاتر کاربر می‌­تواند هزینه محاسباتی بالای روش LSTM را پذیرفته و از این مدل استفاده نماید. همچنین، اگر فرآیند محاسباتی و مدل­سازی سناریو محور مد نظر است مدل HEC-HMS ارجحیت دارد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Comparing the Performance of Deep Learning, Polynomial Neural Network and HEC-HMS Models in Predicting Daily Runoff

نویسندگان English

Sahar Mostafaei 1
Vahid Moosavi 2
Ronny Berndtsson 3
1 MSc Student, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University (TMU),Tehran, Iran
2 Assistant Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University (TMU), Tehran, Iran.
3 Assistant director at Centre for Advanced Middle Eastern Studies (CMES) and Professor at Division of Water Resources Engineering, Lund University, Lund, Sweden.
چکیده English

Estimating runoff caused by excess rainfall at the watershed scale is necessary for precise design of water structures, comprehensive watershed, and flood management. In this research, we evaluated deep learning methods in comparison to polynomial neural networks and HEC-HMS models in three watersheds, i.e., Bar-Erieh, Kasilian, and Latian. For this purpose, daily runoff was simulated using a long short-term memory (LSTM) deep learning model and compared to multi-layer perceptron (MLP) as the most common artificial intelligence model, group method of data handling (GMDH) and HEC-HMS as a physically based model for robust neural network modeling. The results showed that the R2 ranged from 0.872 to 0.986, RMSE from 0.086 m3/s to 2.22 m3/s, and NRMSE from 18.9 to 66.0%. The results indicate that the performance of the MLP model is mediocre with an average NRMSE of 51.2%, the performance of the GMDH model is good with an average NRMSE of 44.6%, and the LSTM model is very good with an average NRMSE of 26.8%. Considering the high computational cost of LSTM compared to the GMDH model, it can be recommended that the GMDH model should be used if the user does not expect very high accuracy and precision, and if higher accuracy is required, the user may need to accept a high computational cost and the LSTM model. Also, if the process and scenario-based modeling are the focus, the HEC-HMS model is preferred.

کلیدواژه‌ها English

Artificial Intelligence
Flood
Modeling
Rainfall-Runoff Process
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