The Institute of Technology Sligo published a new paper on 29th March 2022 in the international journal Sustainability. This is available in open access.
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.
Title: Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale
Authors: Salem Gharbia, Khurram Riaz, Iulia Anton, Gabor Makrai, Laurence Gill, Leo Creedon, Marion McAfee, Paul Johnston, Francesco Pilla
Cite as: Gharbia, S.; Riaz, K.; Anton, I.; Makrai, G.; Gill, L.; Creedon, L.; McAfee, M.; Johnston, P.; Pilla, F. Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale. Sustainability 2022, 14, 4037. https://doi.org/10.3390/su14074037