A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs
In hydrological modelling, artificial neural network (ANN) models have been popular in the scientific community for at least two decades. The current paper focuses on short-term streamflow forecasting, 1 to 7 days ahead, using an ANN model in two northeastern American watersheds, the Androscoggin and Susquehanna. A virtual modelling environment is implemented, where data used to train and validate the ANN model were generated using a deterministic distributed model over 16 summers (2000–2015). To examine how input variables affect forecast accuracy, we compared streamflow forecasts from the ANN model using four different sets of inputs characterizing the watershed state—surface soil moisture, deep soil moisture, observed streamflow the day before the forecast, and surface soil moisture along with antecedent observed streamflow. We found that the best choice of inputs consists of combining surface soil moisture with observed streamflow for the two watersheds under study. Moreover, to examine how the spatial distribution of input variables affects forecast accuracy, we compared streamflow forecasts from the ANN using surface soil moisture at three spatial distributions—global, fully distributed, and single pixel-based—for the Androscoggin watershed. We show that model performance was similar for both the global and fully distributed representation of soil moisture; however, both models surpass the single pixel-based models. Future work includes evaluating the developed ANN model with real observations, quantified in situ or remotely sensed.
Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs
In hydrological modelling, artificial neural network (ANN) models have been popular in the scientific community for at least two decades. The current paper focuses on short-term streamflow forecasting, 1 to 7 days ahead, using an ANN model in two northeastern American watersheds, the Androscoggin and Susquehanna. A virtual modelling environment is implemented, where data used to train and validate the ANN model were generated using a deterministic distributed model over 16 summers (2000–2015). To examine how input variables affect forecast accuracy, we compared streamflow forecasts from the ANN model using four different sets of inputs characterizing the watershed state—surface soil moisture, deep soil moisture, observed streamflow the day before the forecast, and surface soil moisture along with antecedent observed streamflow. We found that the best choice of inputs consists of combining surface soil moisture with observed streamflow for the two watersheds under study. Moreover, to examine how the spatial distribution of input variables affects forecast accuracy, we compared streamflow forecasts from the ANN using surface soil moisture at three spatial distributions—global, fully distributed, and single pixel-based—for the Androscoggin watershed. We show that model performance was similar for both the global and fully distributed representation of soil moisture; however, both models surpass the single pixel-based models. Future work includes evaluating the developed ANN model with real observations, quantified in situ or remotely sensed.
Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs
Renaud Jougla (author) / Robert Leconte (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Probabilistic simulation of surface soil moisture using hydrometeorological inputs
| Taylor & Francis Verlag | 2013
Short-Term Forecast of Oxygen Concentration in Nitrification Chamber Using Artificial Neural Network
| DOAJ | 2022
Operational Hydrometeorological Forecast System for Espírito Santo State, Brazil
| Online Contents | 2017
Operational Hydrometeorological Forecast System for Espírito Santo State, Brazil
| Online Contents | 2015