Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

Lúcio Carlos Pinheiro Campos Filho, Nelio Moura de Figueiredo, Cláudio José Cavalcante Blanco, Maisa Sales Gama Tobias and Paulo Afonso ()
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Lúcio Carlos Pinheiro Campos Filho: Waterway and Port Research Group, Faculty of Naval Engineering (FENAV/ITEC/UFPA), Technological Institute, Federal University of Pará, Belém 66075-110, PA, Brazil
Nelio Moura de Figueiredo: Waterway and Port Research Group, Faculty of Naval Engineering (FENAV/ITEC/UFPA), Technological Institute, Federal University of Pará, Belém 66075-110, PA, Brazil
Cláudio José Cavalcante Blanco: Faculty of Sanitary and Environmental Engineering (FAESA/ITEC/UFPA), Technological Institute, Federal University of Pará, Belém 66075-110, PA, Brazil
Maisa Sales Gama Tobias: Faculty of Naval Engineering (FENAV/ITEC/UFPA), Technological Institute, Federal University of Pará, Belém 66075-110, PA, Brazil
Paulo Afonso: Waterway and Port Research Group, Faculty of Naval Engineering (FENAV/ITEC/UFPA), Technological Institute, Federal University of Pará, Belém 66075-110, PA, Brazil

Sustainability, 2024, vol. 16, issue 19, 1-22

Abstract: The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R 2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

Keywords: cargo capacity; waterway logistics; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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