Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Mosbeh R Kaloop,Mohammed El-Diasty,Jong Wan Hu

doi:10.1080/19475705.2017.1327464

Abstract

ABSTRACTAccurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the least-squares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average.

Highlights

Nowadays, the water level rise is become a high risk for the earth life with temperature change of the earth
Seo et al (2016) utilized the wavelet decomposition to improve the prediction model of daily river stage for the artificial neural network (ANN), and it was found that the root-meansquare error (RMSE) performance of ANN model for the river stage can be improved by 29% with wavelet decomposition applied
water level change (WLC) from three tide gauges, namely Yarmouth (Canada), Saint John (Canada) and Charlottetown (Canada) were used to implement the developed adaptive neuro-fuzzy inference system (ANFIS) model and compare the results with the current-state-of-the-art wavelet neural network (WNN) model to obtain accurate WLC prediction model for maritime applications based on short-period of water level measurements from the three stations

Summary

Introduction

The water level rise is become a high risk for the earth life with temperature change of the earth. The prediction models for sea water level changes with temperature changes and human activities have shown a high correlation between them (Hansen et al 2010; Hay & Mimura 2010; Stocker et al 2015; Tang et al 2016; Aral & Guan 2016). The maritime and coastal engineering applications and future planning around the shore lines of seas or oceans are dependent mainly on the accurate WLCs measurements and predictions. Accurate WLC prediction models are required for maritime applications. Many integrated methods were developed to predict the WLC (Karimi et al 2013; El-diasty & Al-harbi 2015; Kaloop et al 2016). Karimi et al (2013) developed a prediction model that investigated

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Conclusion