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In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model. [ABSTRACT FROM AUTHOR]

Abstract: Correct estimation of suspended sediment concentration carried by a river is very important for many water resources projects. The application of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, for suspended sediment concentration estimation is proposed in this paper. The LGP is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, operated by the US Geological Survey (USGS) are used as case studies. The root mean square errors (RMSE) and determination coefficient (R 2) statistics are used for evaluating the accuracy of the models. Comparison of the results indicated that the LGP performs better than the neuro-fuzzy, neural networks and rating curve models. For the Rio Valenciano and Quebrada Blanca Stations, it is found that the LGP models with RMSE=44.4mg/l, R 2 =0.910 and RMSE=13.9mg/l, R 2 =0.952 in test period is superior in estimating daily suspended sediment concentrations than the best accurate neuro-fuzzy model with RMSE=52.0mg/l, R 2 =0.876 and RMSE=17.9mg/l, R 2 =0.929, respectively. [ABSTRACT FROM AUTHOR]