Assessing the applicability of the Taguchi design method to an interrill erosion study

Zhang, Fengbao; Wang, Z.L.; Yang, Mingyi

doi:10.1016/j.jhydrol.2014.11.059

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…where SS T is the total sum of squares, SS F is the factorial sum of squares, V Er is the variance of error, and DF is the degrees of freedom. Analysis of Variance output gives the values of DF, SS T , SS F , and V Er [32].…”

Section: Percentage Contributionmentioning

confidence: 99%

Change in Soil Particle Size Distribution and Erodibility with Latitude and Vegetation Restoration Chronosequence on the Loess Plateau, China

Song

Entemake

et al. 2020

IJERPH

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Analyzing the dynamics of soil particle size distribution (PSD) and erodibility is important for understanding the changes of soil texture and quality after cropland abandonment. This study aimed to determine how restoration age and latitude affect soil erodibility and the multifractal dimensions of PSD during natural recovery. We collected soil samples from grassland, shrubland, and forests with different restoration ages in the steppe zone (SZ), forest-steppe zone (FSZ), and forest zone (FZ). Various analyses were conducted on the samples, including multifractal analysis and erodibility analysis. Our results showed that restoration age had no significant effect on the multifractal dimensions of PSD (capacity dimension (D0), information dimension (D1), information dimension/capacity dimension ratio (D1/D0), correlation dimension (D2)), and soil erodibility. Multifractal dimensions tended to increase, while soil erodibility tended to decrease, with restoration age. Latitude was negatively correlated with fractal dimensions (D0, D2) and positively correlated with K and D1/D0. During vegetation restoration, restoration age, precipitation, and temperature affect the development of vegetation, resulting in differences in soil organic carbon, total nitrogen, soil texture, and soil enzyme activity, and by affecting soil structure to change the soil stability. This study revealed the impact of restoration age and latitude on soil erosion in the Loess Plateau.

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Section: Percentage Contributionmentioning

confidence: 99%

Change in Soil Particle Size Distribution and Erodibility with Latitude and Vegetation Restoration Chronosequence on the Loess Plateau, China

Song

Entemake

et al. 2020

IJERPH

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“…In a novel approach, Taguchi search was used in this study for selecting the best values of these random parameters. It is a powerful advanced technique that uses orthogonal array and signal to noise ratio (S/N) to minimize the number of experiments and greatly decrease the time, cost, and effort in finding optimal parameters of algorithms (Zhang et al 2015).…”

Section: -9-particle Swarm Optimization (Pso)mentioning

confidence: 99%

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

Seifi¹,

Ehteram²,

Nayebloei³

et al. 2021

Preprint

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In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were investigated and predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve prediction accuracy and reduce uncertainty. Uncertainty analysis was performed using generalized likelihood uncertainty estimation (GLUE), while wavelet coherence was used to assess interactions between Ts and meteorological parameters. For the arid site, ANFIS-SFO (RMSE = 1.18oC, MAE = 1.05oC, NSE = 0.93, PBIAS = 7%, and R2 = 0.9998) produced the most accurate performance at 5 cm soil depth. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6, 18, 18.3, and 18.18 % compared with the respective standalone model. At the semi-humid site, all integrated models showed most accurate performance at 10 cm soil depth, with RMSE for the best model (ANFIS-SFO) increasing by 10.5%, and MAE by 10.1%, from 10 to 30 cm depth. GLUE analysis confirmed that integrating optimization algorithms with machine learning models decreased the uncertainty in Ts predictions. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers.

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“…Sadeghi et al (2012) used the Taguchi method to investigate the contribution rate of different factors on soil erosion and sediment yield. This method was assessed by Zhang et al (2015a) in inter-rill erosion and proved it to be timesaving and laborsaving. To explore the optimal method to quantify contribution, further studies should be performed to compare the contribution rate calculated by the Taguchi method and that calculated by the method adopted in this article.…”

Section: Contribution Rate Of Sediment Load To Soil Detachment Rate Fmentioning

confidence: 99%

Quantifying the Contribution of Sediment Load to Soil Detachment Rate by Sediment‐Laden Rill Flow

Shen

Wang

Zhang

et al. 2017

Soil Science Soc of Amer J

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Sediment load changes with downslope distance during rill erosion process, and thus quantifying the potential contribution of sediment load on soil detachment rate is essential to accurately model the rill erosion process. A standardization-based method was adopted to quantify the contribution for the first time, and the rill flume with a soil-feeding hopper was specifically designed to insulate the effect of sediment load on detachment rate. Loessial soil was quantitatively fed into rill flow to produce different sediment loads. Seven flow discharges were combined with six slopes. Soil detachment rate was measured for each combination under five sediment loads (10, 25, 50, 75, and 90% of the sediment transport capacity, respectively). The results showed that soil detachment rate by sediment-laden rill flow decreased linearly with the increase in sediment load. Stream power is the best hydrodynamic parameter in relation to the detachment rate under different sediment loads compared with shear stress and unit stream power. The comprehensive response relationship of soil detachment rate to sediment load and stream power is a binary linear equation (R 2 = 0.9482). The contribution rate of sediment load to soil detachment rate is 30.43% and that of stream power is 64.39%. The negative effect of sediment load on soil detachment rate accounts for almost one-third of the total contribution. It is important to draw sediment load as a negative factor into process-based rill erosion model. This study can provide a feasible way for researchers to quantify the contribution rate of factors and can help to understand rill erosion process sufficiently.

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Assessing the applicability of the Taguchi design method to an interrill erosion study

Cited by 23 publications

References 31 publications

Change in Soil Particle Size Distribution and Erodibility with Latitude and Vegetation Restoration Chronosequence on the Loess Plateau, China

Change in Soil Particle Size Distribution and Erodibility with Latitude and Vegetation Restoration Chronosequence on the Loess Plateau, China

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

Quantifying the Contribution of Sediment Load to Soil Detachment Rate by Sediment‐Laden Rill Flow

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