{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T20:20:53Z","timestamp":1767903653908,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordination for the Improvement of Higher Education Personnel Foundation (CAPES)"},{"name":"Postgraduate Program in Civil Engineering (PPGEC) of the Federal University of Par\u00e1 (UFPA)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>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 Tapaj\u00f3s 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 R2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.<\/jats:p>","DOI":"10.3390\/su16198517","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:09:52Z","timestamp":1727683792000},"page":"8517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-9767","authenticated-orcid":false,"given":"L\u00facio Carlos Pinheiro","family":"Campos Filho","sequence":"first","affiliation":[{"name":"Waterway and Port Research Group, Faculty of Naval Engineering (FENAV\/ITEC\/UFPA), Technological Institute, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6430-4623","authenticated-orcid":false,"given":"Nelio Moura de","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Waterway and Port Research Group, Faculty of Naval Engineering (FENAV\/ITEC\/UFPA), Technological Institute, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8022-2647","authenticated-orcid":false,"given":"Cl\u00e1udio Jos\u00e9 Cavalcante","family":"Blanco","sequence":"additional","affiliation":[{"name":"Faculty of Sanitary and Environmental Engineering (FAESA\/ITEC\/UFPA), Technological Institute, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0888-0685","authenticated-orcid":false,"given":"Maisa Sales Gama","family":"Tobias","sequence":"additional","affiliation":[{"name":"Faculty of Naval Engineering (FENAV\/ITEC\/UFPA), Technological Institute, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-2491","authenticated-orcid":false,"given":"Paulo","family":"Afonso","sequence":"additional","affiliation":[{"name":"Waterway and Port Research Group, Faculty of Naval Engineering (FENAV\/ITEC\/UFPA), Technological Institute, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"},{"name":"Centro ALGORITMI, Department of Production and Systems, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.jtrangeo.2018.04.021","article-title":"Book Review","volume":"69","author":"Taylor","year":"2018","journal-title":"J. Transp. Geogr."},{"key":"ref_2","first-page":"100466","article-title":"Business Models for Dedicated Container Freight on Swedish Inland Waterways","volume":"35","author":"Williamsson","year":"2020","journal-title":"Res. Transp. Bus. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/03088831003700678","article-title":"Short-Sea Shipping: An Analysis of Its Determinants","volume":"37","author":"Medda","year":"2010","journal-title":"Marit. Policy Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/03088839.2014.904947","article-title":"Short Sea Shipping as Intermodal Competitor: A Theoretical Analysis of European Transport Policies","volume":"42","author":"Trujillo","year":"2015","journal-title":"Marit. Policy Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ajsl.2015.06.006","article-title":"Intermodal Inland Waterway Transport: Modelling Conditions Influencing Its Cost Competitiveness","volume":"31","author":"Wiegmans","year":"2015","journal-title":"Asian J. Shipp. Logist."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijtst.2020.05.002","article-title":"Inland Waterway Transportation (IWT) in Ghana: A Case Study of Volta Lake Transport","volume":"10","author":"Solomon","year":"2020","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1017\/S1740022809990325","article-title":"Why England and Not China and India? Water Systems and the History of the Industrial Revolution","volume":"5","author":"Tvedt","year":"2010","journal-title":"J. Glob. Hist."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.apenergy.2018.06.061","article-title":"Intertemporal Optimization of Synthesis, Design and Operation of Integrated Energy Systems of Ships: General Method and Application on a System with Diesel Main Engines","volume":"226","author":"Sakalis","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.tre.2018.08.013","article-title":"Budgeting Maintenance Dredging Projects under Uncertainty to Improve the Inland Waterway Network Performance","volume":"119","author":"Ahadi","year":"2018","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jprocont.2018.12.017","article-title":"Model Predictive Control and Moving Horizon Estimation for Water Level Regulation in Inland Waterways","volume":"76","author":"Segovia","year":"2019","journal-title":"J. Process Control"},{"key":"ref_11","unstructured":"Teixeira, C.A.N., Rocio, M.A.R., do Amaral, A.P., and d\u2019Oliveira, L.A.S. (2024, May 04). Brazilian Inland Navigation, Available online: https:\/\/web.bndes.gov.br\/bib\/jspui\/bitstream\/1408\/15380\/3\/BS47__NavegacaoInterior_P.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duldner-Borca, B., Hoerandner, L., Bieringer, B., Khanbilverdi, R., and Putz-Egger, L.-M. (2024). New Design Options for Container Barges with Improved Navigability on the Danube. Sustainability, 16.","DOI":"10.20944\/preprints202403.1878.v1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shi, J., Bai, T., Zhao, Z., and Tan, H. (2024). Driving Economic Growth through Transportation Infrastructure: An In-Depth Spatial Econometric Analysis. Sustainability, 16.","DOI":"10.3390\/su16104283"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100575","DOI":"10.1016\/j.clet.2022.100575","article-title":"Challenges and Opportunities for a South America Waterway System","volume":"11","author":"Hunt","year":"2022","journal-title":"Clean. Eng. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vilarinho, A., Liboni, L.B., Cezarino, L.O., Micco, J.D., Mommens, K., and Macharis, C. (2024). Challenges and Opportunities for the Development of Inland Waterway Transport in Brazil. Sustainability, 16.","DOI":"10.3390\/su16052136"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhou, B., Pan, X., Zhang, H., Qian, H., Cheng, W., and Yin, W. (2024). Assessing Waterway Carrying Capacity from a Multi-Benefit Synergistic Perspective. Sustainability, 16.","DOI":"10.3390\/su16114379"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.trd.2016.09.013","article-title":"The Waterway Ship Scheduling Problem","volume":"60","author":"Shi","year":"2018","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"195","DOI":"10.5957\/jspd.2016.32.4.195","article-title":"Emission and Fuel Reduction for Offshore Support Vessels through Hybrid Technology","volume":"32","author":"Lindstad","year":"2016","journal-title":"J. Ship Prod. Des."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.apenergy.2016.08.065","article-title":"Investigating the Implications of a New-Build Hybrid Power System for Roll-on\/Roll-off Cargo Ships from a Sustainability Perspective\u2014A Life Cycle Assessment Case Study","volume":"181","author":"Roskilly","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.energy.2016.07.121","article-title":"Investigation of Diesel Hybrid Systems for Fuel Oil Reduction in Slow Speed Ocean Going Ships","volume":"114","author":"Dedes","year":"2016","journal-title":"Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.oceaneng.2016.05.047","article-title":"Techno Economic and Environmental Assessment of Wind Assisted Marine Propulsion Systems","volume":"121","author":"Talluri","year":"2016","journal-title":"Ocean Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.simpat.2011.12.004","article-title":"Simulation Modeling of the Vessel Traffic in Delaware River: Impact of Deepening on Port Performance","volume":"22","author":"Almaz","year":"2012","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1016\/j.trpro.2017.05.351","article-title":"Analysis to Assess Potential Rivers for Cargo Transport in Indonesia","volume":"25","author":"Fathoni","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1016\/j.asej.2017.08.006","article-title":"Evaluating and Analyzing Navigation Efficiency for the River Nile (Case Study: Ensa-Naga Hamady Reach)","volume":"9","author":"Kamal","year":"2018","journal-title":"Ain Shams Eng. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"04017028","DOI":"10.1061\/(ASCE)HE.1943-5584.0001548","article-title":"Evaluation of Future Streamflow Patterns in Lake Simcoe Subbasins Based on Ensembles of Statistical Downscaling","volume":"22","author":"Kuo","year":"2017","journal-title":"J. Hydrol. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.jhydrol.2018.08.059","article-title":"The Impact of Climate Change on Inland Waterway Transport: Effects of Low Water Levels on the Mackenzie River","volume":"566","author":"Scheepers","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.14710\/gt.v16i1.366","article-title":"Dampak pelayaran kapal laut di alur Sungai Musi","volume":"16","author":"Sugeng","year":"2010","journal-title":"Gema Teknol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.wse.2020.03.002","article-title":"Responses of River Bed Evolution to Flow-Sediment Process Changes after Three Gorges Project in Middle Yangtze River: A Case Study of Yaojian Reach","volume":"13","author":"Zuo","year":"2020","journal-title":"Water Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"124701","DOI":"10.1016\/j.jhydrol.2020.124701","article-title":"Evolution of Reversal of the Lowest Low Waters in a Tidal River Network","volume":"585","author":"Luo","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ifacol.2018.06.196","article-title":"Efficient River Management Using Stochastic MPC and Ensemble Forecast of Uncertain In-Flows","volume":"51","author":"Nasir","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102159","DOI":"10.1016\/j.trd.2019.10.012","article-title":"Forecasting the Impacts of Climate Change on Inland Waterways","volume":"82","author":"Christodoulou","year":"2020","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.ifacol.2016.07.626","article-title":"Constraint Satisfaction Problem Based on Flow Graph to Study the Resilience of Inland Navigation Networks in a Climate Change context","volume":"49","author":"Nouasse","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118531","DOI":"10.1016\/j.envres.2024.118531","article-title":"Analysing the Performance of the NARX Model for Forecasting the Water Level in the Chikugo River Estuary, Japan","volume":"251","author":"Vidyalashmi","year":"2024","journal-title":"Environ. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e25989","DOI":"10.1016\/j.heliyon.2024.e25989","article-title":"Evaluation of Deep Learning Computer Vision for Water Level Measurements in Rivers","volume":"10","author":"Liu","year":"2024","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"130025","DOI":"10.1016\/j.jhydrol.2023.130025","article-title":"A Combined Hydrodynamic Model and Deep Learning Method to Predict Water Level in Ungauged Rivers","volume":"625","author":"Li","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"128419","DOI":"10.1016\/j.jhydrol.2022.128419","article-title":"Assessment of the Joint Impact of Rainfall and River Water Level on Urban Flooding in Wuhan City, China","volume":"613","author":"Wang","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101070","DOI":"10.1016\/j.ejrh.2022.101070","article-title":"Application of Satellite and Reanalysis Precipitation Products for Hydrological Modeling in the Data-Scarce Porij\u00f5gi Catchment, Estonia","volume":"41","author":"Moges","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_38","first-page":"100316","article-title":"Extreme Rainfall Events in Amazonia: The Madeira River Basin","volume":"18","author":"Moreira","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1007\/s00704-018-2672-5","article-title":"Modeling Climate Change Impacts on Precipitation in Arid Regions of Pakistan: A Non-Local Model Output Statistics Downscaling Approach","volume":"137","author":"Ahmed","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ahmed, K., Shahid, S., Wang, X., Nawaz, N., and Khan, N. (2019). Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan. Water, 11.","DOI":"10.3390\/w11020210"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1007\/s00382-016-3284-3","article-title":"Rainfall Trends in the South Asian Summer Monsoon and Its Related Large-Scale Dynamics with Focus over Pakistan","volume":"48","author":"Latif","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/qj.3244","article-title":"Validation of the CHIRPS Satellite Rainfall Estimates over Eastern Africa","volume":"144","author":"Dinku","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The Climate Hazards Infrared Precipitation with Stations\u2014A New Environmental Record for Monitoring Extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.atmosres.2016.11.006","article-title":"Evaluating Satellite-Derived Long-Term Historical Precipitation Datasets for Drought Monitoring in Chile","volume":"186","author":"Zambrano","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.ajsl.2018.09.008","article-title":"The Ship Management Firm Selection: The Case of South Korea","volume":"34","author":"Seo","year":"2018","journal-title":"Asian J. Shipp. Logist."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1016\/j.ejor.2019.08.002","article-title":"Optimising Cargo Loading and Ship Scheduling in Tidal Areas","volume":"280","author":"Ferson","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2166\/nh.2016.264","article-title":"Comparison of Random Forests and Other Statistical Methods for the Prediction of Lake Water Level: A Case Study of the Poyang Lake in China","volume":"47","author":"Li","year":"2016","journal-title":"Hydrol. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2084","DOI":"10.1080\/02626667.2015.1083650","article-title":"Neural Network Model for Discharge and Water-Level Prediction for Ramganga River Catchment of Ganga Basin, India","volume":"61","author":"Khan","year":"2016","journal-title":"Hydrol. Sci. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103656","DOI":"10.1016\/j.advwatres.2020.103656","article-title":"Combining Statistical Machine Learning Models with ARIMA for Water Level Forecasting: The Case of the Red River","volume":"142","author":"Phan","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_50","first-page":"111","article-title":"Simula\u00e7\u00e3o de Vaz\u00f5es e N\u00edveis de \u00c1gua M\u00e9dios Mensais Para o Rio Tapaj\u00f3s Usando Modelos ARIMA","volume":"19","author":"Figueiredo","year":"2014","journal-title":"Rev. Bras. Recur. Hidr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"04020044","DOI":"10.1061\/(ASCE)WW.1943-5460.0000609","article-title":"Computational Tool for Sizing and Optimization of Planimetric Geometric Parameters of Inland Navigation Channels and of Port Access in Brazil","volume":"147","author":"Barbosa","year":"2021","journal-title":"J. Waterw. Port Coast. Ocean Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Garcia, F.C.C., Retamar, A.E., and Javier, J.C. (2016, January 22\u201325). Development of a Predictive Model for On-Demand Remote River Level Nowcasting: Case Study in Cagayan River Basin, Philippines. Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore.","DOI":"10.1109\/TENCON.2016.7848657"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1007\/s11063-020-10363-z","article-title":"Forecasting Monthly Tourism Demand Using Enhanced Backpropagation Neural Network","volume":"52","author":"Wang","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"104804","DOI":"10.1016\/j.catena.2020.104804","article-title":"Reversal of the Sediment Load Increase in the Amazon Basin Influenced by Divergent Trends of Sediment Transport from the Solim\u00f5es and Madeira Rivers","volume":"195","author":"Li","year":"2020","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"107526","DOI":"10.1016\/j.oceaneng.2020.107526","article-title":"A Multi-Layer Perceptron Approach for Accelerated Wave Forecasting in Lake Michigan","volume":"211","author":"Feng","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.chnaes.2022.09.004","article-title":"Multilayer Perceptron Neural Network Based Models for Prediction of the Rainfall and Reference Crop Evapotranspiration for Sub-Humid Climate of Dapoli, Ratnagiri District, India","volume":"43","author":"Hunasigi","year":"2022","journal-title":"Acta Ecol. Sin."},{"key":"ref_57","unstructured":"Haykin, S.S. (1999). Neural Networks: A Comprehensive Foundation, Prentice Hall. [2nd ed.]."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jhydrol.2010.02.019","article-title":"Development and Application of a Decision Group Back-Propagation Neural Network for Flood Forecasting","volume":"385","author":"Chen","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_59","unstructured":"Petrosyan, A., Dereventsov, A., and Webster, C.G. (2020, January 16\u201319). Neural Network Integral Representations with the ReLU Activation Function. Proceedings of the First Mathematical and Scientific Machine Learning Conference, Virtual."},{"key":"ref_60","first-page":"1875","article-title":"Nonparametric Regression Using Deep Neural Networks with ReLU Activation Function","volume":"48","year":"2020","journal-title":"Ann. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"100620","DOI":"10.1016\/j.disopt.2020.100620","article-title":"Complexity of Training ReLU Neural Network","volume":"44","author":"Boob","year":"2020","journal-title":"Discret. Optim."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.procs.2018.04.239","article-title":"Research on Convolutional Neural Network Based on Improved Relu Piecewise Activation Function","volume":"131","author":"Lin","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"83","DOI":"10.4186\/ej.2019.23.6.83","article-title":"Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures","volume":"23","author":"Azad","year":"2019","journal-title":"Eng. J."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, J., Shi, P., Jiang, P., Hu, J., Qu, S., Chen, X., Chen, Y., Dai, Y., and Xiao, Z. (2017). Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting. Water, 9.","DOI":"10.3390\/w9010048"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S1464-1909(01)85008-5","article-title":"Modelling Sediment Transfer in Malawi: Comparing Backpropagation Neural Network Solutions against a Multiple Linear Regression Benchmark Using Small Data Sets","volume":"26","author":"Abrahart","year":"2001","journal-title":"Phys. Chem. Earth Part B Hydrol. Ocean. Atmos."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Sheng, H. (2010, January 17\u201319). Rainfall Prediction Using Generalized Regression Neural Network: Case Study Zhengzhou. Proceedings of the 2010 International Conference on Computational and Information Sciences, Chengdu, China.","DOI":"10.1109\/ICCIS.2010.312"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jhydrol.2016.11.033","article-title":"An Emotional ANN (EANN) Approach to Modeling Rainfall-Runoff Process","volume":"544","author":"Nourani","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1007\/s11600-019-00330-1","article-title":"Long Short-Term Memory (LSTM) Recurrent Neural Network for Low-Flow Hydrological Time Series Forecasting","volume":"67","author":"Sahoo","year":"2019","journal-title":"Acta Geophys."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jhydrol.2012.11.017","article-title":"Comparison of the ARMA, ARIMA, and the Autoregressive Artificial Neural Network Models in Forecasting the Monthly Inflow of Dez Dam Reservoir","volume":"476","author":"Valipour","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.eswa.2005.04.034","article-title":"Comparison of Neural Networks and Regression Analysis: A New Insight","volume":"29","author":"Kumar","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1002\/hyp.445","article-title":"Improving Extreme Hydrologic Events Forecasting Using a New Criterion for Artificial Neural Network Selection","volume":"15","author":"Coulibaly","year":"2001","journal-title":"Hydrol. Process."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Barrass, C.B., and Derrett, D.R. (2012). Hydrostatic Curves and Values for Vessels Initially Having Trim by the Bow or by the Stern. Ship Stability for Masters and Mates, Elsevier.","DOI":"10.1016\/B978-0-08-097093-6.00026-8"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Biran, A., and L\u00f3pez-Pulido, R. (2014). Hydrostatic Curves. Ship Hydrostatics and Stability, Elsevier.","DOI":"10.1016\/B978-0-08-098287-8.00004-9"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Tupper, E.C. (2013). Structures. Introduction to Naval Architecture, Elsevier.","DOI":"10.1016\/B978-0-08-098237-3.00013-8"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.tra.2016.11.023","article-title":"The Determinants of Vessel Capacity Utilization: The Case of Brazilian Iron Ore Exports","volume":"110","author":"Adland","year":"2018","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_77","unstructured":"Campos Filho, L.C.P., Moura de Figueiredo, N., Pantoja Barbosa, F.G., Saavedra, R.d.S., Filgueiras, T., and Pav\u00e3o de Souza, P.A. (2019). Analysis of Geometric Conformation of the Lower Tapaj\u00f3s Stretch Using Navigation Channel Sizing Software, Galoa."},{"key":"ref_78","unstructured":"PIANC (2014). Harbour Approach Channels Design Guidelines, PIANC Report No. 121, PIANC."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Briggs, M.J., Vantorre, M., Uliczka, K., and Debaillon, P. (2018). Prediction of Squat for Underkeel Clearance. Handbook of Coastal and Ocean Engineering, World Scientific.","DOI":"10.1142\/9789813204027_0036"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1061\/(ASCE)WW.1943-5460.0000176","article-title":"Validation of a Risk-Based Numerical Model for Predicting Deep-Draft Underkeel Clearance","volume":"139","author":"Briggs","year":"2013","journal-title":"J. Waterw. Port Coast. Ocean Eng."},{"key":"ref_81","unstructured":"Eryuzlu, N.E., Cao, Y.L., and D\u2019Agnolo, F. (1994, January 10). Underkeel Requirements for Large Vessels in Shallow Waterways. Proceedings of the 28th International Navigation Congress, Sevilla, Spain."},{"key":"ref_82","unstructured":"Capitania Fluvial de Porto Velho (2024, May 04). Normas e Procedimentos espec\u00edficos para a jurisdi\u00e7\u00e3o da Capitania Fluvial de Porto Velho. Available online: https:\/\/www.marinha.mil.br\/cfpv\/?q=conteudo\/normas-e-procedimentos-especificos-para-jurisdicao-da-capitania-fluvial-de-porto-velho."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/16\/19\/8517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:07:26Z","timestamp":1760112446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/16\/19\/8517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":82,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["su16198517"],"URL":"https:\/\/doi.org\/10.3390\/su16198517","relation":{},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]}}}