Thermal behavior estimation of a solar wall operated by TiO2 nanofluids using several machine learning models

Neto, Juarez Pompeu de Amorim; Marinho, Felipe Pinto; Lima, Ricardo José Pontes; Rocha, Paulo Alexandre Costa; Mendonça, Stéphano Praxedes; Bueno, André Valente; Silva, Maria Eugênia Vieira da

doi:10.1007/s40430-022-03425-x

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…In [86], SHAP analysis showed that forecasting streamflow relies on important precipitation data, besides streamflow inputs, often influencing the mode positively. In Ekmekcio glu et al [87] and Aydin and Iban [88], SHAP analysis showed that precipitation may affect the forecasted result differently depending on the ML model used, which is an expected behavior since different ML approaches process data differently, resulting in different results for an identical task [33,48,49,89].…”

Section: Discussionmentioning

confidence: 98%

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Oliveira Santos,

Costa Rocha,

Scott

et al. 2023

Water

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Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and evolution of these events and create an advanced warning system to allow for the proper implementation of preventive measures. This work introduced a new graph-based forecasting model, namely, graph neural network sample and aggregate (GNN-SAGE), to estimate river flooding. It then validated the proposed model in the Humber River watershed in Ontario, Canada. Using past precipitation and stage data from reference and neighboring stations, the proposed GNN-SAGE model could estimate the river stage for flooding events up to 24 h ahead, improving its forecasting performance by an average of 18% compared with the persistence model and 9% compared with the graph-based model residual gated graph convolutional network (GNN-ResGated), which were used as baselines. Furthermore, GNN-SAGE generated smaller errors than those reported in the current literature. The Shapley additive explanations (SHAP) revealed that prior data from the reference station was the most significant factor for all prediction intervals, with seasonality and precipitation being more influential for longer-range forecasts. The findings positioned the proposed GNN-SAGE model as a cutting-edge solution for flood forecasting and a valuable resource for devising early flood-warning systems.

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