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.
Supporting sustainable development for the urban environment is crucial in the age of rapid urbanisation. Air pollution modelling is one of the key tools for researchers, scientists, and urban planners to understand pollution behaviour. Recent updates in air quality regulations are challenging the state-of-the-art air pollution modelling techniques by requiring accurate predictions on a high temporal level, i.e. predictions at the hourly level rather than the annual level. Current stateof-the-art models designed to have good prediction accuracy on the low temporal resolution by assuming that the pollution is in steady state. Making predictions on higher temporal resolution violates this assumption and causing inaccurate predictions. We introduce a novel statistical regression based air pollution model which produces accurate hourly predictions by using data with high temporal resolution and advanced regression algorithms. We conducted an analysis which shows that the state-of-the-art evaluation techniques (e.g. RMSE) do not describe the nature of the mispredictions of the models built on different data subsets. We carried out an extensive input data evaluation experiment where we concluded that our approach could achieve further accuracy improvement by training the models on a carefully selected subset of the input data.
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