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

Amir Hossein Zaji, Hossein Bonakdari () and Bahram Gharabaghi
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Amir Hossein Zaji: Razi University
Hossein Bonakdari: Razi University
Bahram Gharabaghi: University of Guelph

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 12, No 11, 4215-4230

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11269-019-02356-y

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