Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan
المؤلفون المشاركون
Sibtain, Muhammad
Li, Xianshan
Nabi, Ghulam
Azam, Muhammad Imran
Bashir, Hassan
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-01
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources.
Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task.
To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series.
Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction.
CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity.
Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose.
Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP.
The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively.
However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively.
The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction.
The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff.
Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sibtain, Muhammad& Li, Xianshan& Nabi, Ghulam& Azam, Muhammad Imran& Bashir, Hassan. 2020. Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1153390
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sibtain, Muhammad…[et al.]. Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1153390
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sibtain, Muhammad& Li, Xianshan& Nabi, Ghulam& Azam, Muhammad Imran& Bashir, Hassan. Development of a Three-Stage Hybrid Model by Utilizing a Two-Stage Signal Decomposition Methodology and Machine Learning Approach to Predict Monthly Runoff at Swat River Basin, Pakistan. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1153390
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153390
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر