TY  - GEN 
AU  - Ruiz Águilar, Juan Jesús
AU  - Moscoso López, José Antonio
AU  - Urda, Daniel
AU  - González Enrique, Francisco Javier
AU  - Turias Domínguez, Ignacio José
A4  - Ingeniería Industrial e Ingeniería Civil
A4  - Ingeniería Informática
PY  - 2020
SN  - 2076-3417
UR  - http://hdl.handle.net/10498/24315
AB  - An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a...
LA  - eng
PB  - MDPI
KW  - maritime transport
KW  - container forecasting
KW  - support vector regression
KW  - self-organizing maps
KW  - machine learning
KW  - hybrid models
TI  - A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities
DO  - 10.3390/app10238326
ER  -