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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2019-121
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-121
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Apr 2019

03 Apr 2019

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This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Characterization of Hillslope Hydrologic Events Using Machine Learning Algorithms

Eunhyung Lee and Sanghyun Kim Eunhyung Lee and Sanghyun Kim
  • Department of Environmental Engineering, College of Engineering, Pusan National University, Busan, South Korea

Abstract. Time series of soil moisture were measured at 30 points for 396 rainfall events on a steep, forested hillslope between 2007 and 2016. We then analyzed the dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between weighting components of a self-organizing map (SOM). Generation patterns of two primary hillslope hydrological processes, namely, vertical flow and lateral flow, at the upslope and downslope areas were responsible for the distinction of the hydrologic events. Two-dimensional spatial weighting patterns in the SOM provided explanations for the relationships between rainfall characteristics and hydrological processes at different locations and depths. High reliability in hydrologic classification was achieved for both the driest and wettest events; as assessed through k-fold cross validation using 10 years of data. Representative soil moisture monitoring points were found through temporal stability analysis of the event structure delineated from the machine learning classification. Application of a supervised machine learning algorithm provided a scheme using soil moisture for the cluster identification of hydrologic event even without rainfall data which is useful to configure hillslope hydrologic process with the least cost in data acquisition.

Eunhyung Lee and Sanghyun Kim

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Eunhyung Lee and Sanghyun Kim

Eunhyung Lee and Sanghyun Kim

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Latest update: 26 Nov 2020
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Short summary
A big dataset of hydrologic events (rainfall and soil moisture) at a hillslope can be characterized by machine learning algorithm. An unsupervised machine learning algorithm was used to cluster the hydrologic events. The hillslope hydrological processes, vertical flow, and lateral flow were responsible for the distinctions between the event clusters. A supervised machine learning algorithm provided a decision tree to identify cluster of events without rainfall and antecedent soil moisture.
A big dataset of hydrologic events (rainfall and soil moisture) at a hillslope can be...
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