Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Abstract

Forecasting the dam displacement is an important problem in dam monitoring. By using multi-temporal monitoring data to make predictions and forecast dam structure health, it is possible to detect abnormalities over time of the dam so that remedial measures can be taken promptly to minimize risks. The paper proposes a method of applying the interval type-2 fuzzy logic system (IT2FLS) and ant colony optimization (ACO) technique to forecast hydroelectric dams’ displacement. The method consists of two stages: The first is to design an IT2FLS according to the input and output data with base rules and newly constructed membership functions. The second is to find the optimal parameters for an interval type-2 fuzzy logic system based on the ACO technique and give the final fuzzy forecasting system. Experimental data are measured from hydroelectricity monitoring data of Ialy, Gia Lai Province, Vietnam, in the period from 2004 to 2016. The proposed method is compared with some other methods to compare prediction efficiency and accuracy. The experimental results show that the proposed model has the best performance compared to other standard methods in both accuracy and stability of prediction. This shows that a predictive model based on the IT2FLS is a promising method and can be used for forecasting time-series data.