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Water Resources Management

, Volume 33, Issue 12, pp 4215–4230 | Cite as

Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System

  • Amir Hossein Zaji
  • Hossein BonakdariEmail author
  • Bahram Gharabaghi
Article
  • 75 Downloads

Abstract

This study presents a novel method for more accurate forecasting freshwater Lake Levels with complex fluctuation patterns due to multiple anthropogenic demands and climate factors. The new method employs the mighty King’s Castle Optimization (KCO) with Training Sample Adaption (TSA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to develop a novel hybrid KCO-TSA-ANFIS model. The performance of the new KCO-TSA-ANFIS Lake water level forecast model is tested on the monthly water levels of Lake Van, in Turkey, showing significantly improved accuracy in model forecasts compared with the regular ANFIS model. By comparing the Root Mean Square Error (RMSE) results, it can be concluded that the KCO-TSA-ANFIS method has 71% higher performance than the simple ANFIS method.

Keywords

Adaptive neuro-fuzzy inference system Hybrid method King’s castle optimization Lake water level Training dataset adaptation 

Notes

Compliance with Ethical Standards

Conflict of Interest

None.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringRazi UniversityKermanshahIran
  2. 2.School of EngineeringUniversity of GuelphGuelphCanada

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