Comparison of Conventional Deterministic and Entropy-Based Methods for Predicting Sediment Concentration in Debris Flow

Zhu, Zhongfan; Wang, Hongrui; Pang, Bo; Dou, Jie; Peng, Danling

doi:10.3390/w11030439

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Apart from the fatalities, they also cause huge economic losses by damaging properties such as buildings, bridges and roads; this trend is observed more than any other natural disasters, such as earthquakes, typhoons, heat waves, sinkhole collapses, floods and forest fires [3][4][5][6]. The increased amount of urbanization and economic development together with the unusual frequency of severe regional precipitations owing to global climate change, the landslide hazard losses are expected to rise in the future [7][8][9]. To mitigate and reduce the economic losses and risks associated with the landslide hazards, there is an urgent requirement to identify and map the landslide-prone areas.…”

Section: Introductionmentioning

confidence: 99%

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

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Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

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Section: Introductionmentioning

confidence: 99%

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

et al. 2019

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“…[ 47 ] developed an EBM to identify seasonally floods and partitioning the entire flood season into multiple sub‐seasons. In a study by Zhu et al., [ 48 ] the distribution of sediment concentration and the mean sediment concentration in debris flow was estimated using Tsallis entropy and Shannon entropy. In this paper, the EBM was proposed to separate the driving force for the evolution of the estuary nutrient loads.…”

Section: Methodsmentioning

confidence: 99%

Separating the Driving Force on Estuary Nutrient Evolution

Wang

Yuan

Yan

et al. 2020

CLEAN Soil Air Water

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The Yangtze river estuary (YRE) of China is selected as the research area. The evolution of nutrient load in YRE from 2004 to 2016 is examined against field investigated data. The entropy value-based method is proposed to decouple the forces driving the dynamics of the nutrients. The results indicated that: 1) The nutrient driving force sequence in YRE is mainstream boundary (MB, 34.57%) > regional pollutant boundary (PB, 34.48%) > self-purification (SP, 20.26%) > sediment release (SR, 7.00%) > atmospheric deposition (AD, 3.68%). 2) The strength of the MB is weakened by the decreasing distance to the entrance of the river and the geographical position is significantly correlated with the force of the PB. 3) The weight of SP range from 13.8% to 28.3% and the seasons have important influence for SP force. In flood seasons SP is strengthened by 12.3% on average. 4) SR and AD do not play a significant role in the nutrient evolution, but forces fluctuated with space-time conditions. 5) As different forms of nutrients undergo different physical, chemical, and biological processes in water, the forces varied with nutrient factors.

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“…The fitting accuracy improves as R decreases. Relative error analysis has been frequently adopted and confirmed to be a good statistical method to compare the prediction accuracy of the developed models by some researchers [15,[17][18][19][20]24,25,[27][28][29].…”

Section: Selected Experimental Datamentioning

confidence: 99%

“…As Singh et al [16] have reviewed, the Tsallis entropy theory together with the principle of maximum entropy have been widely applied to solve certain typical water and environmental engineering problems. For example, the Tsallis entropy theory has been adopted by many researchers to estimate the one-dimensional and two-dimensional velocity distributions of open channels [17][18][19][20], the potential rate of infiltration in unsaturated soils [21][22][23], the vertical distribution of suspended sediment concentration [24,25], the flow-duration curve [26], the sediment concentration distribution in debris flow [27,28], and the thickness of the bed-load layer in an open channel [29]. In these studies, the Tsallis entropy-based model has showed a high prediction accuracy with experimental data, suggesting that the probability method based on the Tsallis entropy theory could be a good addition to some existing deterministic models for approaching certain water and environmental engineering problems [16].…”

Section: Introductionmentioning

confidence: 99%

An Expression for Velocity Lag in Sediment-Laden Open-Channel Flows Based on Tsallis Entropy Together with the Principle of Maximum Entropy

Zhu

Dou

et al. 2019

Entropy

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In the context of river dynamics, some experimental results have shown that particle velocity is different from fluid velocity along the stream-wise direction for uniform sediment-laden open-channel flows; this velocity difference has been termed velocity lag in the literature. In this study, an analytical expression for estimating the velocity lag in open-channel flows was derived based on the Tsallis entropy theory together with the principle of maximum entropy. The derived expression represents the velocity lag as a function of a non-dimensional entropy parameter depending on the average and maximum values of velocity lag from experimental measurements. The derived expression was tested against twenty-two experimental datasets collected from the literature with three deterministic models and the developed Shannon entropy-based model. The Tsallis entropy-based model agreed better with the experimental datasets than the deterministic models for eighteen out of the twenty-two total real cases, and the prediction accuracy for the eighteen experimental datasets was comparable to that of the developed Shannon entropy-based model (the Tsallis entropy-based expression agreed slightly better than the Shannon entropy-based model for twelve out of eighteen test cases, whereas for the other six test cases, the Shannon entropy-based model had a slightly higher prediction accuracy). Finally, the effects of the friction velocity of the flow, the particle diameter, and the particles’ specific gravity on the velocity lag were analyzed based on the Tsallis entropy-based model. This study shows the potential of the Tsallis entropy theory together with the principle of maximum entropy to predict the stream-wise velocity lag between a particle and the surrounding fluid in sediment-laden open-channel flows.

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Comparison of Conventional Deterministic and Entropy-Based Methods for Predicting Sediment Concentration in Debris Flow

Cited by 5 publications

References 22 publications

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

Separating the Driving Force on Estuary Nutrient Evolution

An Expression for Velocity Lag in Sediment-Laden Open-Channel Flows Based on Tsallis Entropy Together with the Principle of Maximum Entropy

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