Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale

Salem Gharbia, Khurram Riaz, Iulia Anton, Gabor Makrai, Laurence Gill, Leo Creedon, Marion McAfee, Paul Johnston and Francesco Pilla
Additional contact information
Salem Gharbia: Department of Environmental Science & Centre for Environmental Research Innovation and Sustainability (CERIS), Institute of Technology Sligo, F91 YW50 Sligo, Ireland
Khurram Riaz: Department of Environmental Science & Centre for Environmental Research Innovation and Sustainability (CERIS), Institute of Technology Sligo, F91 YW50 Sligo, Ireland
Iulia Anton: Department of Environmental Science & Centre for Environmental Research Innovation and Sustainability (CERIS), Institute of Technology Sligo, F91 YW50 Sligo, Ireland
Gabor Makrai: Department of Computer Science, The University of York, York YO10 5DD, UK
Laurence Gill: Department of Civil, Structural and Environmental Engineering, Trinity College, D02 PN40 Dublin, Ireland
Leo Creedon: Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Institute of Technology Sligo, F91 YW50 Sligo, Ireland
Marion McAfee: Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Institute of Technology Sligo, F91 YW50 Sligo, Ireland
Paul Johnston: Department of Civil, Structural and Environmental Engineering, Trinity College, D02 PN40 Dublin, Ireland
Francesco Pilla: Department of Planning and Environmental Policy, University College Dublin (UCD), D04 V1W8 Dublin, Ireland

Sustainability, 2022, vol. 14, issue 7, 1-23

Abstract: Changes in streamflow within catchments can have a significant impact on agricultural production, as soil moisture loss, as well as frequent drying and wetting, may have an effect on the nutrient availability of many soils. In order to predict future changes and explore the impact of different scenarios, machine learning techniques have been used recently in the hydrological sector for simulation streamflow. This paper compares the use of four different models, namely artificial neural networks (ANNs), support vector machine regression (SVR), wavelet-ANN, and wavelet-SVR as surrogate models for a geophysical hydrological model to simulate the long-term daily water level and water flow in the River Shannon hydrological system in Ireland. The performance of the models has been tested for multi-lag values and for forecasting both short- and long-term time scales. For simulating the water flow of the catchment hydrological system, the SVR-based surrogate model performs best overall. Regarding modeling the water level on the catchment scale, the hybrid model wavelet-ANN performs the best among all the constructed models. It is shown that the data-driven methods are useful for exploring hydrological changes in a large multi-station catchment, with low computational cost.

Keywords: catchment hydrological system; hydrology; machine learning; SVR; temporal downscaling; wavelet-ANN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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