Artificial Intelligence Techniques for Predicting Tidal Effects Based on Geographic Locations in Ghana

Abstract

Tidal forces as a result of attraction of external bodies (Sun, Moon and Stars) through gravity and are a source of noise in many geoscientific field observations. The solid earth tides cause deformation. This deformation results in displacement in geographic positions on the surface of the earth. The displacement due to tidal effects can result in deformation of engineering structures, loss of lives, and economic cost. Tidal forces also help in detecting other environmental and tectonic signals. This study quantifies the effects of solid earth tides on stationary survey controls in five regions of Ghana. The study is in two stages: firstly, the solid earth tides were estimated for each control by a geometric approach (combining Navier’s equation of motion and Love theories). Secondly, estimation using two artificial intelligence methods (Multivariate Adaptive Regression Splines (MARS) and Backpropagation Artificial Neural Network (ANN)). Based on statistical indices of Mean Square Error (MSE) and Correlation Coefficient (R), BPANN, and MARS models can be used as a realistic alternative technique in quantifying solid earth tides for the study area. The MSE and R (MSE; BPANN = 1.3249 × 10–04 and MSE; MARS = 2.2052 × 10–06; R; BPANN = –0.6067 and R; MARS 0.6570) values indicate that MARS outperforms BPANN in quantifying solid earth tides in the study area. BPANN and MARS can be used as an efficient tool for quantifying tidal values based on geographic positions for geodetic deformation studies within the study area.

A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

“…Further research advances in the field of artificial intelligence for tide prediction were reported by [21][22][23][24]. The Long Short Term Memory (LSTM) model performed optimally for 1 h ahead prediction of water level during a storm surge in the Yangtze River Estuary in the East Sea and could make a 15 h ahead prediction with limited error [21].…”

“…Further research advances in the field of artificial intelligence for tide prediction were reported by [21][22][23][24]. The Long Short Term Memory (LSTM) model performed optimally for 1 h ahead prediction of water level during a storm surge in the Yangtze River Estuary in the East Sea and could make a 15 h ahead prediction with limited error [21]. In another study [22], tested the predictive ability of the Non-linear Autoregressive Exogenous (NARX), neural network models, while considering meteorological data, astronomical tides, and lagged value of observed sea level data to forecast extreme values of high tides in the Venice Lagoon.…”