EXTREME LEARNING MACHINE PREDICTS HIGH-FREQUENCY STREAM FLOW AND NITRATE-N CONCENTRATIONS IN A KARST AGRICULTURAL WATERSHED

Timothy McGill, William Isaac Ford

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Efforts to reduce nitrogen contributions from karst agroecosystems have had variable success, in part due to an incomplete understanding of nitrogen source, fate, and transport dynamics in karst watersheds. Recent advancements in environmental sensors and data-driven artificial intelligence models may be useful in improving our understanding of system behavior and the linkages between soil hydrologic processes and karst nitrate loading dynamics. We collected 35 months of high-resolution streamflow, nitrate-N concentration, soil moisture and temperature (from 10-100 cm depths), and meteorological data in a karst agricultural watershed in the Inner-Bluegrass region of Central Kentucky. Two-layer extreme learning machine (TELM) models were developed to predict nitrate-N concentrations and flow rates as a function of meteorological and soil parameter inputs. Results suggest tight linkages between soil moisture gradients at different depths and nitrate-N concentrations at the watershed outlet. TELM modeling results supported visual observations from the high-frequency data and suggest that inclusion of both soil moisture and temperature parameters at all soil depths improved predictions of both flow rate and nitrate-N concentration (with optimal NSE values of 0.93 and 0.94, respectively, when all inputs were considered). Hysteresis analysis suggested that inclusion of the deepest soil layer (100 cm) was necessary to predict hysteresis observed during storm events. The findings of the study highlight the importance of variable activation of matrix waters in preferential flows throughout events and seasons and its subsequent impacts on nitrate-N concentrations. Results suggest that management models should incorporate vertical variability in soil hydrology to accurately characterize nitrate source and transport dynamics. Further, the results of hysteresis analysis underscore the importance of inclusion of hysteresis indices, in addition to typical model evaluation statistics, to ensure accurate representation of nutrient flow pathways.

Original languageEnglish
Pages (from-to)73-87
Number of pages15
JournalJournal of the ASABE
Volume67
Issue number2
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 American Society of Agricultural and Biological Engineers.

Keywords

  • Extreme learning machine
  • Karst agroecosystem
  • Nitrate
  • Water resources

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science

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