|LIANG, JING - University Of California|
|LI, WENZHE - University Of Southern California|
|SIMUNEK, JIRI - University Of California|
Submitted to: Water
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2019
Publication Date: 1/24/2019
Citation: Liang, J., Li, W., Bradford, S.A., Simunek, J. 2019. Physics-informed data-driven models to predict surface runoff water quantity and quality in agricultural fields. Water. 11(2):200. https://doi.org/10.3390/w11020200.
Interpretive Summary: Computer models are normally used to simulate runoff water quantity and quality from agricultural fields. Most of these models are based on physical descriptions of water flow and pollution transport that are difficult or time-consuming to solve. Simpler data-driven models, which can also include available experimental measurements, were investigated to simulate runoff behavior. Results revealed that some data-driven models can be developed to accurately and rapidly simulate runoff water quantity and quality in agricultural fields. This information will be of interest to scientists, engineers, and government regulators concerned with predicting agricultural runoff in an accurate and efficient manner.
Technical Abstract: Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models (PBMs), which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with PBMs, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. Here we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport. A large number of numerical simulations was then carried out to develop a database containing information about the impact of various relevant factors on surface runoff quantity and quality, such as different weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices. Finally, the resulting database was used to train data-driven models. Several Machine Learning techniques were explored to find input-output functional relations. The results indicate that the Neural Network model with two hidden layers performed the best among selected data-driven models, accurately predicting runoff water quantity and quality over a wide range of parameters.