BBAuthors: Bean, E.Z.; Advisor: -; Participants: Huffaker, R.G.; Migliaccio, K.W. (2018)
We evaluated competing approaches for automated soil water cycles analysis that use widely available R packages based on pattern recognition and machine learning (findpeaks [R‐FP], symbolic aggregate approximation [R‐SAX], and density histogram [R‐DH]), and a MATLAB code based on soil water dynamic principles (SWDP). These approaches were applied to three SMS datasets. Our empirical results showed superiority of R‐SAX for identifying valid soil water cycles, probably due to benefiting from training sets to calibrate to correct cycles. Two other approaches (SWDP and R‐FP) provided similar results without need of training sets or preprocessing data. Three approaches for estimating field... 
