Juan F. Farfán-Durán
Centre for Technological Innovations in Construction and Civil Engineering (CITEEC)
| Subject Areas: | Artificial neural networks, Hydrological Modelling, Hydrology, Water Resources Management, Hydroinformatics |
Recent Activity
ABSTRACT:
This data represents outcomes from experiments focused on optimizing a distributed hydrological model via a Surrogate-Assisted Evolutionary Algorithm (SAEA). Two scripts perform data analysis, visualization, and strategy comparison. The first script imports pertinent datasets, visualizes the model's performance, and applies the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to create a confidence band for predictions. The second script manages data for multiple generations and compares Monte Carlo (MC) and Evolutionary Algorithm with Surrogate Modeling (EA-SM) strategies, visualized in a scatter plot. Together, these scripts provide comprehensive insights into the model's performance, efficiency, and potential enhancements.
ABSTRACT:
MHIA is a continuous hydrological model that computes a balance of the volume of water in the soil, taking into account the following processes: precipitation, infiltration, percolation, evapotranspiration and exfiltration. From these variables, the model evaluates surface and subsurface runoff, generating a hydrograph at the outlet of the modelled catchment. As input data, the model needs to be fed with time series of precipitation and temperature, with whatever time resolution. The model has 14 parameters that must be defined by the user or calibrated from observed discharge time series.
Contact
| (Log in to send email) |
| All | 2 |
| Collection | 0 |
| Resource | 2 |
| App Connector | 0 |
Created: May 31, 2021, 7:58 a.m.
Authors: Farfán-Durán, Juan F. · Luis Cea
ABSTRACT:
MHIA is a continuous hydrological model that computes a balance of the volume of water in the soil, taking into account the following processes: precipitation, infiltration, percolation, evapotranspiration and exfiltration. From these variables, the model evaluates surface and subsurface runoff, generating a hydrograph at the outlet of the modelled catchment. As input data, the model needs to be fed with time series of precipitation and temperature, with whatever time resolution. The model has 14 parameters that must be defined by the user or calibrated from observed discharge time series.
Created: July 25, 2023, 12:38 p.m.
Authors: Farfán-Durán, Juan F. · Cea, Luis
ABSTRACT:
This data represents outcomes from experiments focused on optimizing a distributed hydrological model via a Surrogate-Assisted Evolutionary Algorithm (SAEA). Two scripts perform data analysis, visualization, and strategy comparison. The first script imports pertinent datasets, visualizes the model's performance, and applies the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to create a confidence band for predictions. The second script manages data for multiple generations and compares Monte Carlo (MC) and Evolutionary Algorithm with Surrogate Modeling (EA-SM) strategies, visualized in a scatter plot. Together, these scripts provide comprehensive insights into the model's performance, efficiency, and potential enhancements.