کاربرد مدل ترکیبی شبکه عصبی مصنوعی و الگوریتم‌های بهینه‌سازی فرا ابتکاری در پیش‌بینی شاخص خشکسالی SPEI12

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم و مهندسی آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 دانشیار، گروه مهندسی آب، دانشگاه زنجان، زنجان، ایران.

3 گروه مهندسی عمران، واحد شهرقدس، دانشگاه آزاد اسلامی، تهران، ایران.

4 استادیار، گروه علوم و مهندسی آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

خشکسالی یکی از مهم‌ترین بلایای طبیعی می‌باشد که در همه‌ی رژیم‌های آب و هوایی رخ می‌دهد. بنابراین، پیش‌بینی و مقابله با آن از اهمیت بالایی برخوردار است. در پژوهش حاضر از سه الگوریتم‌های بهینه‌سازی هوشمند (الگوریتم بهینه‌سازی مبتنی بر آموزش و یادگیری (TLBO)، الگوریتم بهینه‌سازی علف‌های هرز (IWO)، الگوریتم ازدحام ذرات (PSO)) و الگوریتم متداول لونبرگ- مارکوات به‌منظور آموزش شبکه عصبی مصنوعی چند لایه، برای پیش‌بینی شاخص خشکسالی SPEI12 یک الی سه ماه آینده در 79 ایستگاه سینوپتیک کشور استفاده گردید. با توجه به تعداد زیاد ایستگاه‌های سینوپتیک، ایستگاه‌ها با توجه به سری‌های زمانی خشکسالی و با استفاده از روش K-means به پنج خوشه C1 تا C5 تقسیم شدند. نتایج با توجه به قرارگیری ایستگاه‌ها در خوشه‌ها مورد مقایسه قرار گرفتند و دقت مدل‌ها بر اساس آماره‌های RMSE) و (R2 داده‌های آزمون، مورد ارزیابی قرار گرفتند. نتایج به‌دست ‌آمده از این پژوهش نشان داد که در هر سه مدل پیش‌بینی با افزایش مقیاس زمانی پیش‌بینی دقت مدل‌ها کاهش یافته است. مقایسه بین سه الگوریتم بهینه‌سازی ذکر شده و الگوریتم لونبرگ- مارکوات به‌عنوان یک الگوریتم پرکاربرد در بهینه‌سازی وزن‌های شبکه عصبی، نشان‌دهنده برتری قابل توجه الگوریتم‌های بهینه‌سازی فراابتکاری است. مقایسه بین سه الگوریتم TLBO،IWO و PSO نشان داد که الگوریتم TLBO اندکی بهتر از سایر الگوریتم‌ها عمل می‌کند و نتایج دقیق‌تری را ارائه می‌کند. بهترین پیش‌بینی مدل‌های ذکر شده و بیشترین مقادیر R2 در خوشه یک (شرق، نوار جنوب و جنوب شرقی ایران) و بیشترین مقادیر RMSE و کمترین دقت مدل‌ها در خوشه پنج (نوار شمالی کشور) مشاهده شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Application of Combined Artificial Neural Network Model and meta-heuristic Optimization Algorithms in Predicting SPEI12 Drought Index

نویسندگان [English]

  • Porya Ghasemi 1
  • Masoud Karbasi 2
  • Alireza Zamani Nouri 3
  • Mahdi Sarai Tabrizi 4
1 Ph.D. Candidate, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Associate Professor, Water Engineering Department, University of Zanjan. Zanjan, Iran
3 Department of Civil Engineering, Shar-e-Qods Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

Drought is one of the most important and damaging natural disasters in the field of water resources that occurs in all climatic regimes of the country. Therefore, predicting and dealing with it is very important. In the present study, 79 synoptic stations in Iran were selected as the study. Three meta-heuristic optimization algorithms TLBO, IWO, PSO and the conventional Levenberg-Marquadt algorithm were used to train the multilayer artificial neural network to predict the SPEI12 drought index for the next one to three months. Due to the large number of synoptic stations, the stations were divided into five clusters C1 to C5 according to the time series of the drought using the K-means method. The results were compared with respect to the location of the stations in the clusters and the accuracy of the models was evaluated based on the RMSE and R2 indices of the test data. Showed that in all three prediction models, the accuracy of the models decreased with increasing prediction time. Comparison between the three optimization algorithms mentioned and Levenberg-Marquadt algorithm as a widely used algorithm in optimizing neural network weights, showed the better performance of meta-heuristic algorithms. The comparison between the three TLBO, IWO and PSO algorithms showed that the TLBO algorithm performed slightly better than the other algorithms and provided more accurate results. R2 was observed in cluster one (eastern regions, southern strip and southeastern regions of Iran) and the highest RMSE values and the lowest accuracy of the models were observed in cluster five (northern strip strip of the country).

کلیدواژه‌ها [English]

  • Drought
  • Intelligence optimization algorithms
  • Machine learning
  • Standardized precipitation evapotranspiration index

مراجع

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مدیریت آب و آبیاری
دوره 11، شماره 2
شهریور 1400
صفحه 173-188

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