Multi-objective optimization of soil erosion parameters using response surface method (RSM) in the Emamzadeh watershed

Shirazi, Mojtaba; Khademalrasoul, Ataallah; Ardebili, Seyed Mohammad Safieddin

doi:10.1007/s11600-020-00404-5

Cited by 10 publications

(6 citation statements)

References 55 publications

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Section: Approaches To Limit Line Estimation – a Practitioner Viewmentioning

confidence: 99%

“…RSM is a collection of mathematical and statistical techniques that is useful for the approximation and optimisation of multivariate stochastic models of 3D surfaces. For example, Shirazi et al (2020) applied RSM techniques to multivariate data to fit optimal maximum response surfaces related to factors controlling soil erosion using an objective function they termed the desirability function. Eberhardt and Thomas (1991), considering environmental systems, recommend the Box and Lucas method to obtain optimal parameter estimates of response surfaces; thus, effectively defining limit lines.…”

Section: Approaches To Limit Line Estimationa Practitioner Viewmentioning

confidence: 99%

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Fitting limit lines (envelope curves) to spreads of geoenvironmental data

Carling

Jonathan

2021

Progress in Physical Geography: Earth and Environment

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Geoscientists frequently are interested in defining the overall trend in x- y data clouds using techniques such as least-squares regression. Yet often the sample data exhibits considerable spread of y-values for given x-values, which is itself of interest. In some cases, the data may exhibit a distinct visual upper (or lower) ‘limit’ to a broad spread of y-values for a given x-value, defined by a marked reduction in concentration of y-values. As a function of x-value, the locus of this ‘limit’ defines a ‘limit line’, with no (or few) points lying above (or below) it. Despite numerous examples of such situations in geoscience, there has been little consideration within the general geoenvironmental literature of methods used to define limit lines (sometimes termed ‘envelope curves’ when they enclose all data of interest). In this work, methods to fit limit lines are reviewed. Many commonly applied methods are ad-hoc and statistically not well founded, often because the data sample available is small and noisy. Other methods are considered which correspond to specific statistical models offering more objective and reproducible estimation. The strengths and weaknesses of methods are considered by application to real geoscience data sets. Wider adoption of statistical models would enhance confidence in the utility of fitted limits and promote statistical developments in limit fitting methodologies which are likely to be transformative in the interpretation of limits. Supplements, a spreadsheet and references to software are provided for ready application by geoscientists.

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Section: Approaches To Limit Line Estimation – a Practitioner Viewmentioning

confidence: 99%

Section: Approaches To Limit Line Estimationa Practitioner Viewmentioning

confidence: 99%

Fitting limit lines (envelope curves) to spreads of geoenvironmental data

Carling

Jonathan

2021

Progress in Physical Geography: Earth and Environment

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“…Response surface models(RMS) and multi-objective genetic algorithms(MOGA) have been widely used in the engineering field as global optimal solutions [11,12] . Shirazi et al(2020) uses response sur-face to optimize soil erosion parameters in watersheds [13] . Eskandari et al(2022) uses the response surface to optimize the cutting speed, feed rate, feed velocity, and depth of cut with the objective functions of minimum cutting force, flank wear, and maximum material removal rate [14] .…”

Section: Introductionmentioning

confidence: 99%

Structural design and parameter optimization of vector brackets for vertical take-off and landing unmanned Aerial Vehicle

Liu,

Quan,

Wang

et al. 2024

Preprint

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By carrying out a central composite design (CCD) test, 49 sample points of the four particular factors were constructed. The lift-to-drag ratio and thrust coefficient were simulated under different structural parameter combinations using the Computational Fluid Dynamics (CFD) method. CFD simulation was verified by carrying out a wind tunnel test, and the results revealed that the thrust coefficient error was less than 9% while the lift-to-drag ratio error was less than 8%. The response surface methodology (RSM) for the lift-to-drag ratio and thrust coefficient was established using a Kriging algorithm. A multi-objective genetic algorithm (MOGA) was used to optimize the parameters with regard to the maximum lift-to-drag ratio and maximum thrust coefficient. The optimal structural parameters were valve diameter at 31 mm, vector bracket length at 51 mm, fixed bracket width at 69 mm, and fixed bracket length at 168. Compared to the original model, the thrust coefficient increased by 19%, and the lift-to-drag ratio did not decrease.

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“…The Pearson product-moment correlation measured the strength of the association between population density and COVID-19 positive cases. In order to evaluate the significance of results, we used the RSM process in which these two variables were found to affect the response of interest (Said et al 2014;Shirazi et al 2020). To represent the data analysis and maps, Thiessen polygons (TM) or Voronoi polygons is carried out for better understanding (Schumann, 1998).…”

Section: Introductionmentioning

confidence: 99%

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Arif

Sengupta

2020

Environ Dev Sustain

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The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

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Multi-objective optimization of soil erosion parameters using response surface method (RSM) in the Emamzadeh watershed

Cited by 10 publications

References 55 publications

Fitting limit lines (envelope curves) to spreads of geoenvironmental data

Fitting limit lines (envelope curves) to spreads of geoenvironmental data

Structural design and parameter optimization of vector brackets for vertical take-off and landing unmanned Aerial Vehicle

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

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