TY - JOUR
T1 - Tree-based modeling methods to predict nitrate exceedances in the Ogallala aquifer in Texas
AU - Uddameri, Venkatesh
AU - Silva, Ana Luiza Bessa
AU - Singaraju, Sreeram
AU - Mohammadi, Ghazal
AU - Hernandez, E. Annette
N1 - Funding Information:
Funding: Partial support provided by the Ogallala Aquifer Program, an USDA-ARS led project including scientists from Kansas State University, Texas A&M AgriLife. Texas Tech University and West Texas A&M University, seeking solutions to problems arising from declining water availability from the Ogallala Aquifer (agreement number 58-3090-9-006).
Funding Information:
Partial support provided by the Ogallala Aquifer Program, an USDA-ARS led project including scientists from Kansas State University, Texas A&M AgriLife. Texas Tech University and West Texas A&M University, seeking solutions to problems arising from declining water availability from the Ogallala Aquifer (agreement number 58-3090-9-006). Comments and suggestions from two anonymous reviewers is gratefully acknowledged. The support from Brazil Scientific Mobility Program to the second author (A.L.B. S) is noted with appreciation.
Publisher Copyright:
© 2020 by the authors.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.
AB - The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.
KW - Aquifer vulnerability
KW - CART
KW - Gradient boosting algorithms
KW - MARS
KW - Machine learning
KW - Nitrate
KW - Ogallala aquifer
KW - Random forests
KW - Water quality
UR - http://www.scopus.com/inward/record.url?scp=85086630107&partnerID=8YFLogxK
U2 - 10.3390/W12041023
DO - 10.3390/W12041023
M3 - Article
AN - SCOPUS:85086630107
VL - 12
JO - Water (Switzerland)
JF - Water (Switzerland)
SN - 2073-4441
IS - 4
M1 - 1023
ER -