Characterization of runoff generation in a mountainous hillslope according to multiple threshold behavior and hysteretic loop features

Lee, Eun-Hyung; Kim, Sanghyun

doi:10.1016/j.jhydrol.2020.125534

Cited by 14 publications

(11 citation statements)

References 51 publications

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“…CARTs do not use any assumptions regarding parameters but instead focus on the repetitive division of the dataset to build a decision tree. A CART can handle various inputs, such as numerical, categorical, and binary inputs; therefore, CARTS can be used for hydrological datasets [25]. The CART algorithm uses the binary recursive partitioning approach to split datasets until they become homogeneous clusters set based on a certain threshold.…”

Section: A Classification and Regression Trees (Carts)mentioning

confidence: 99%

“…Additionally, whereas a neural network is almost identical to a black box that provides results without a clear explanation, tree-based models provide an understandable interpretation for visualization [24]. Classification and regression trees (CARTs) have been used to solve hydrological problems, such as runoff generation [25] and streamflow forecasting [26]. Random forest (RF) algorithms have also been used for runoff and streamflow simulation [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

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Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Nguyen

Heo

et al. 2021

IEEE Access

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The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

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Section: A Classification and Regression Trees (Carts)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Nguyen

Heo

et al. 2021

IEEE Access

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“…Antecedent soil moisture (ASM) plays an essential role in the hydrological response at the hillslope scale (Hardie et al, 2011;Lee and Kim, 2020;Uber et al, 2018). The interaction between the spatial distribution of ASM and rainfall events determines various hydrological processes, such as the occurrence of preferential flow, soil moisture variation patterns, subsurface stormflow, and runoff generation (Bachmair et al, 2012;Saffarpour et al, 2016;Wiekenkamp et al, 2016;Zhang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Kim

2021

Hydrol. Earth Syst. Sci.

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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.

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“…Antecedent soil moisture (ASM) plays an essential role in the hydrological response at the hillslope scale (Hardie et al, 2011;Uber et al, 2018;Lee and Kim, 2020). The interaction between https://doi.org/10.5194/hess-2021-107 Preprint.…”

Section: Introductionmentioning

confidence: 99%

“…the spatial distribution of ASM and rainfall events, determines various hydrological processes, such as the occurrence of preferential flow, soil moisture variation patterns, subsurface stormflow, and runoff generation (Bachmair et al, 2012;Zhang et al, 2011;Saffarpour et al, 2016;Wiekenkamp et al, 2016). The wetter ASM and the greater rainfall events resulted in a greater variation in soil moisture and deeper rainwater percolation (Zhu et al, 2014;Lai et al, 2016;Lee and Kim 2020).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Hillslope Hydrologic Events through a Self-Organizing Map

Lee

Kim

2021

Preprint

Self Cite

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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.

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Characterization of runoff generation in a mountainous hillslope according to multiple threshold behavior and hysteretic loop features

Cited by 14 publications

References 51 publications

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

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

Characterization of Hillslope Hydrologic Events through a Self-Organizing Map

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