Next Article in Journal
Performance of A Two-Dimensional Hydraulic Model for the Evaluation of Stranding Areas and Characterization of Rapid Fluctuations in Hydropeaking Rivers
Next Article in Special Issue
Water Infiltration and Surface Runoff in Steep Clayey Soils of Olive Groves under Different Management Practices
Previous Article in Journal
Applications of Computational Fluid Dynamics in The Design and Rehabilitation of Nonstandard Vertical Slot Fishways
Previous Article in Special Issue
Field Water Balance Closure with Actively Heated Fiber-Optics and Point-Based Soil Water Sensors
Open AccessFeature PaperArticle

Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields

1
Department of Environmental Sciences, University of California, Riverside, CA 92521, USA
2
Department of Computer Sciences, University of Southern California, Los Angeles, CA 90089, USA
3
USDA, ARS, US Salinity Laboratory, Riverside, CA 92507, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(2), 200; https://doi.org/10.3390/w11020200
Received: 28 December 2018 / Revised: 15 January 2019 / Accepted: 20 January 2019 / Published: 24 January 2019
(This article belongs to the Special Issue Soil Hydrology in Agriculture)
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. View Full-Text
Keywords: machine learning; surface runoff; contaminant transport; physically-based model; agriculture field; synthetic data; HYDRUS-1D machine learning; surface runoff; contaminant transport; physically-based model; agriculture field; synthetic data; HYDRUS-1D
Show Figures

Graphical abstract

MDPI and ACS Style

Liang, J.; Li, W.; Bradford, S.A.; Šimůnek, J. Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields. Water 2019, 11, 200.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop