Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application
المؤلفون المشاركون
Al-Ansari, Nadhir
Yaseen, Zaher Mundher
Faris, Hossam
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-21
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably.
The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem.
This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow.
Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study.
Different input combinations are applied for constructing the predictive models based on antecedent values.
The results are evaluated based on several statistical measures and graphical presentations.
The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models.
Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model.
In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yaseen, Zaher Mundher& Faris, Hossam& Al-Ansari, Nadhir. 2020. Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application. Complexity،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1144145
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yaseen, Zaher Mundher…[et al.]. Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application. Complexity No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1144145
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yaseen, Zaher Mundher& Faris, Hossam& Al-Ansari, Nadhir. Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application. Complexity. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1144145
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144145
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