Mean sea level modelling using the neural network along the Chennai coast

Abstract

Abstract This study focuses on the trend analysis of sea level data along the Chennai coast and thereby checks the structural change in the dataset using the Chow method. This study also proposed a methodology for predicting the mean sea level with the feed-forward neural network (FFNN) and wavelet transform neural network (WTNN) models. The data analysis shows that a breakpoint is observed in the year 1994 and found an overall increasing trend during the selected time period at the Chennai coast. For model development, a better understanding of the influencing parameters of the sea level is essential. Hence, correlation analyses have been performed and found that wind speed, sea surface salinity, and surface pressure are influencing variables for modelling sea level data. Apparently, these influencing variables have been considered as potential inputs for model development. To compare the performance of all the developed models, the Root Mean Square Error, Correlation Coefficient, and Nash–Sutcliffe Efficiency (NSE) were utilized. The results of performance indices and the graphical indicators also show that WTNN Model 4 outperformed all the other developed models. It was noticed that the percentage increase in the efficiency of NSE was 29.52% for WTNN Model 4 as compared to other developed models.