Updating the neural network sediment load models using different sensitivity analysis methods: a regional application

Asheghi, Reza; Hosseini, Seyed Abbas; Saneie, Mojtaba; Shahri, Abbas Abbaszadeh

doi:10.2166/hydro.2020.098

Cited by 154 publications

(32 citation statements)

References 52 publications

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“…and fit C (ψ) as an empirical covariance to a known covariance function (Equation ( 8)) to determine the correlation radii in zonal and meridional in each region, the results of which are shown in Figure 3. The sensitivity of the inputs in modeling should be considered [36].…”

Section: Optimal Interpolation Methodsmentioning

confidence: 99%

Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

Wu¹,

Huang²,

Shi³

et al. 2021

Applied Sciences

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Mean dynamic topography (MDT) is crucial for research in oceanography and climatology. The optimal interpolation method (OIM) is applied to MDT modeling, where the error variance–covariance information of the observations is established. The global geopotential model (GGM) derived from GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) gravity data and the mean sea surface model derived from satellite altimetry data are combined to construct MDT. Numerical experiments in the Kuroshio over Japan show that the use of recently released GOCE-derived GGM derives a better MDT compared to the previous models. The MDT solution computed based on the sixth-generation model illustrates a lower level of root mean square error (77.0 mm) compared with the ocean reanalysis data, which is 2.4 mm (5.4 mm) smaller than that derived from the fifth-generation (fourth-generation) model. This illustrates that the accumulation of GOCE data and updated data preprocessing methods can be beneficial for MDT recovery. Moreover, the results show that the OIM outperforms the Gaussian filtering approach, where the geostrophic velocity derived from the OIM method has a smaller misfit against the buoy data, by a magnitude of 10 mm/s (17 mm/s) when the zonal (meridional) component is validated. This is mainly due to the error information of input data being used in the optimal interpolation method, which may obtain more reasonable weights of observations than the Gaussian filtering method.

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Section: Optimal Interpolation Methodsmentioning

confidence: 99%

Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

Wu¹,

Huang²,

Shi³

et al. 2021

Applied Sciences

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“…CNN is combined with strategic location sampling. A DNN was proposed in (Asheghi et al, 2020) to understand various transportation trends during the pandemic.…”

Section: Other Impactsmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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“…Well controls are fixed in this study. However, optimized well control is necessary [87]. Furthermore, deciding the area of oil wells and operational settings (controls, for example, infusion/recuperation rates for heterogeneous supplies) presents extremely troublesome improvement issues and significantly affects underground energy recuperation execution and monetary worth.…”

Section: Limitations Of the Studymentioning

confidence: 99%

A Modified Niching Crow Search Approach to Well Placement Optimization

et al. 2021

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Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

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Updating the neural network sediment load models using different sensitivity analysis methods: a regional application

Cited by 154 publications

References 52 publications

Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

Battling COVID-19 using machine learning: A review

A Modified Niching Crow Search Approach to Well Placement Optimization

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