DYNA JOURNAL ENGINEERING

Key words:

hybrid neural network, maritime environments, prediction of CO2 concentration, training vessels, model validation and generalisation, LSTM CNN MLP, multivariate time series forecasting, marine CO2 monitoring, shipboard sensors, environmental monitoring systems, mean absolute error, temporal spatial patterns, deep learning regression

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

This study presents a hybrid neural network architecture incorporating LSTM, CNN and MLP layers for predicting CO? concentrations in marine environments. The proposed architecture uses 96 input variables and generates 48 output variables, enabling it to efficiently capture temporal and spatial patterns in the analysed data. Cross-validation with five folds was implemented to assess the generalisability of the previously validated model. Additionally, new time series data from four different locations on three Navy training vessels operating in the same area were incorporated to test the model's performance in real conditions. The statistical techniques applied proved effective and accurate. In training conditions, the model achieved a MAE of 8.5-12.5 ppm and a MAPE of 2-3%, demonstrating its effectiveness in environmental monitoring in training boats.