Application of Neural Networks for Hydrologic Process Understanding at a Midwestern Watershed

• Published in: Hydrology Vol. 10; no. 2; p. 27
• Main Authors: Aliev, Annushka, Koya, Sinan Rasiya, Kim, Incheol, Eun, Jongwan, Traylor, Elbert, Roy, Tirthankar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Annual variations; artificial neural network; Artificial neural networks; Cattle; Coastal inlets; Conservation; Conservation practices; Discharge; Drought; drought analysis; Environmental aspects; Flood frequency; flood frequency analysis; Flood peak; Flood predictions; Flooding; Floods; Frequency analysis; Hydraulic measurements; Hydrologic models; Hydrology; Nebraska; Neural networks; Performance prediction; Rain and rainfall; rainfall-runoff modeling; Rainfall-runoff relationships; River discharge; Rivers; Runoff; Snow; Water quality; watershed hydrology; Watersheds; Nebraska; United States > US
• ISSN: 2306-5338; 2306-5338
• DOI: 10.3390/hydrology10020027

The Shell Creek Watershed (SCW) is a rural watershed in Nebraska with a history of chronic flooding. Beginning in 2005, a variety of conservation practices have been employed in the watershed. Those practices have since been credited with attenuating flood severity and improving water quality in SCW. This study investigated the impacts of 13 different controlling factors on flooding at SCW by using an artificial neural network (ANN)-based rainfall-runoff model. Additionally, flood frequency analysis and drought severity analysis were conducted. Special emphasis was placed on understanding how flood trends change in light of conservation practices to determine whether any relation exists between the conservation practices and flood peak attenuation, as the strategic conservation plan implemented in the watershed provides a unique opportunity to examine the potential impacts of conservation practices on the watershed. The ANN model developed in this study showed satisfactory discharge–prediction performance, with a Kling–Gupta Efficiency (KGE) value of 0.57. It was found that no individual controlling variable used in this study was a significantly better predictor of flooding in SCW, and therefore all 13 variables were used as inputs, which resulted in the satisfactory ANN model discharge–prediction performance. Furthermore, it was observed that after conservation planning was implemented in SCW, the magnitude of anomalous peak flows increased, while the magnitude of annual peak flows decreased. However, more comprehensive assessment is necessary to identify the relative impacts of conservation practices on flooding in the basin.