Characterization of Hillslope Hydrologic Events through a Self-Organizing Map

Moisture Conditions ◽

Hillslope Scale ◽

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (in 10 locations with 3 varying depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. Soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, maximum variation, and standard deviation of peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classification in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided greater insight on relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events can be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns made by combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.

Characterization of soil moisture response patterns and hillslope hydrological processes through a self-organizing map

Hydrology and Earth System Sciences ◽

10.5194/hess-25-5733-2021 ◽

2021 ◽

Vol 25 (11) ◽

pp. 5733-5748

Author(s):

Eunhyung Lee ◽

Sanghyun Kim

Keyword(s):

Soil Moisture ◽

Learning Algorithm ◽

Hydrological Processes ◽

Peak Time ◽

Response Patterns ◽

Self Organizing Map ◽

Moisture Conditions ◽

Hillslope Scale ◽

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (at 10 locations with three different depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. The soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, soil moisture difference index, and standard deviation of the peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classifications in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided more insights into the relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events could be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns attributable to combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.

Characterization of Hillslope Hydrologic Events Using Machine Learning Algorithms

10.5194/hess-2019-121 ◽

2019 ◽

Author(s):

Eunhyung Lee ◽

Sanghyun Kim

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Learning Algorithm ◽

High Reliability ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Hydrological Processes ◽

Machine Learning Algorithm ◽

Self Organizing Map ◽

Machine Learning Classification

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.

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

BioMed Research International ◽

10.1155/2015/535894 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kwang Baek Kim ◽

Chang Won Kim

Keyword(s):

Fatty Liver ◽

Hepatic Steatosis ◽

Learning Algorithm ◽

Fat Content ◽

Liver Fat ◽

Self Organizing Map ◽

Liver Fat Content ◽

Computer Aided ◽

Good Set ◽

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

Characterization of variability of air particulate matter size profiles recorded by optical particle counters near a complex emissive source by use of Self-Organizing Map algorithm

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2019.05.008 ◽

2019 ◽

Vol 190 ◽

pp. 48-54 ◽

Cited By ~ 2

Author(s):

S. Licen ◽

S. Cozzutto ◽

G. Barbieri ◽

M. Crosera ◽

G. Adami ◽

...

Keyword(s):

Particulate Matter ◽

Self Organizing Map ◽

Use Of Self ◽

Air Particulate Matter ◽

Map Algorithm ◽

Air Particulate ◽

ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378) ◽

An improved learning algorithm for laterally interconnected synergetically self-organizing map

10.1109/iconip.1999.843996 ◽

2003 ◽

Author(s):

B.-L. Zhang ◽

T.D. Gedeon

Keyword(s):

Learning Algorithm ◽

Self Organizing Map ◽

Improved Learning ◽

Characterization of Powder- and Tablet Properties of Different Direct Compaction Grades of Mannitol Using a Kohonen Self-organizing Map and a Lasso Regression Model

Journal of Pharmaceutical Sciences ◽

10.1016/j.xphs.2020.05.010 ◽

2020 ◽

Vol 109 (8) ◽

pp. 2585-2593

Author(s):

Atsushi Kosugi ◽

Kok Hoong Leong ◽

Hinako Tsuji ◽

Yoshihiro Hayashi ◽

Shungo Kumada ◽

...

Keyword(s):

Regression Model ◽

Self Organizing Map ◽

Lasso Regression ◽

Tablet Properties ◽

Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA

10.5194/egusphere-egu2020-19674 ◽

2020 ◽

Author(s):

Maria Nezi ◽

Ioannis Tsoukalas ◽

Charalampos Ntigkakis ◽

Andreas Efstratiadis

Keyword(s):

Soil Moisture ◽

Daily Rainfall ◽

Extreme Rainfall ◽

Annual Rainfall ◽

Event Analysis ◽

Daily Streamflow ◽

Accumulated Rainfall ◽

Moisture Conditions ◽

Parameter Values

<p>Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.</p>

Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

Hydrology and Earth System Sciences ◽

10.5194/hess-15-3171-2011 ◽

2011 ◽

Vol 15 (10) ◽

pp. 3171-3179 ◽

Cited By ~ 30

Author(s):

Y. Zhang ◽

H. Wei ◽

M. A. Nearing

Keyword(s):

Soil Moisture ◽

Standard Deviation ◽

Soil Layer ◽

Arid Environment ◽

Storm Events ◽

Runoff Generation ◽

Small Watersheds ◽

Runoff Modeling ◽

Moisture Conditions

Abstract. This study presents unique data on the effects of antecedent soil moisture on runoff generation in a semi-arid environment, with implications for process-based modeling of runoff. The data were collected from four small watersheds measured continuously from 2002 through 2010 in an environment where evapo-transpiration approaches 100% of the infiltrated water on the hillslopes. Storm events were generally intense and of short duration, and antecedent volumetric moisture conditions were dry, with an average in the upper 5 cm soil layer over the nine year period of 8% and a standard deviation of 3%. Sensitivity analysis of the model showed an average of 0.05 mm change in runoff for each 1% change in soil moisture, indicating an approximate 0.15 mm average variation in runoff accounted for by the 3% standard deviation of measured antecedent soil moisture. This compared to a standard deviation of 4.7 mm in the runoff depths for the measured events. Thus the low variability of soil moisture in this environment accounts for a relative lack of importance of storm antecedent soil moisture for modeling the runoff. Runoff characteristics simulated with a nine year average of antecedent soil moisture were statistically identical to those simulated with measured antecedent soil moisture, indicating that long term average antecedent soil moisture could be used as a substitute for measured antecedent soil moisture for runoff modeling of these watersheds. We also found no significant correlations between measured runoff ratio and antecedent soil moisture in any of the four watersheds.

On the Relationship of Soil Moisture and Extreme Temperatures in East China

Earth Interactions ◽

10.1175/2013ei000551.1 ◽

2014 ◽

Vol 18 (1) ◽

pp. 1-20 ◽

Cited By ~ 26

Author(s):

Lei Meng ◽

Yanjun Shen

Keyword(s):

Soil Moisture ◽

Standardized Precipitation Index ◽

Heat Fluxes ◽

East China ◽

Daily Minimum Temperature ◽

Surface Temperatures ◽

Quantile Regression Analysis ◽

Temperature Indices ◽

Moisture Conditions

Abstract Soil moisture conditions affect energy partitioning between sensible and latent heat fluxes, resulting in a change in surface temperatures. In this study, the relationships between antecedent soil moisture conditions [as indicated by the 6-month standardized precipitation index (SPI)] and several temperature indices are statistically quantified using the quantile regression analysis across East China to investigate the influence of soil moisture on summer surface temperatures. These temperature indices include percentage of hot days (%HD), heat-wave duration (HWD), daily temperature range (DTR), and daily minimum temperature (Tmin). It was demonstrated that soil moisture had a significant impact on %HD and HWD at higher quantiles in all regions but the east, suggesting that drier soil moisture conditions tend to intensity summer hot extremes. It was also found that hot extremes (%HD and HWD at higher quantiles) had increased substantially from 1958 to 2010. Soil moisture also significantly affected the DTR in all regions but tended to have more impacts on the DTR in soil moisture-limited regimes than in energy-limited regimes. This study provides observational evidence of soil moisture influences on hot extremes in East China.