Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Abstract

Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.

Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression

“…network parameters which is computationally prohibitive for most modern neural networks. The same is true for methods based on Markov Chain Monte Carlo sampling [46,48]. In [45] input uncertainty is treated via a Gaussian process approximation to a neural network, which however, requires, in theory, an infinite width of the network.…”