Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

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

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.

Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

Suburban growth and its impacts on surface runoff were investigated using the soil conservation service curve number (SCS-CN) model, compared with the integrated advanced remote sensing and geographic information system (GIS)-based integrated approach, over South Kingston, Rhode Island, USA. This study analyzed and employed the supervised classification method on four Landsat images from 1994, 2004, 2014, and 2020 to detect land-use pattern changes through remote sensing applications. Results showed that 68.6% urban land expansion was reported from 1994 to 2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN model was developed to examine suburban growth and surface runoff estimation. The developed model demonstrated the spatial distribution of runoff for each of the studied years. The results showed an increasing spatial pattern of 2% to 10% of runoff from 1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the significant impact of suburban growth in surface runoff over the last 36 years in South Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and agricultural land (4.8% area of the total area). Suburban growth began after 2000, and within 16 years this land-use change started to show its substantial impact on surface runoff. We concluded that the proposed integrated approach could classify land-use and land cover information to understand suburban growth and its potential impact on the area.