Field Data Recovery of Tidal Level Using a Neuro-Genetic Algorithm

Abstract

This paper presents validation results of the neuro-genetic algorithm with conventional network structure and recursive implementation. The aim of the research is to recover the missing data of tidal level in the vicinity of the Chao Phraya river mouth, in Thailand. This data recovery system (DRS) is based on the use of transfer function approach where the system response is constructed by the concept of learning from experiences. The genetic algorithm (GA) was used to find the optimum number of units in the hidden layer. Sensitivity test of input units was performed by trials. A self recovery and a spatial recovery are investigated at two tidal stations. It is found that the obtained network outperforms the harmonic analysis and can be used in real practice. In general, the efficiency index of the design network is found more than 0.90. Overall, the NN model reproduces the time series tidal level data in the missing window. The use of conventional network structure with known data set at the neighboring station in the spatial recovery gives better results than the use of recursive architecture in the self recovery.