Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach

Dassanayake, S.M.; Mousa, Ahmad; Fowmes, Gary John; Susilawati, S.; Zamara, Katarzyna A.

doi:10.1016/j.geotexmem.2022.08.005

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…NARX network was used for time series modeling in various fields recently [56][57][58]. NARX model is a recurrent dynamic neural network that relates the current value of a time series to the two following values:…”

Section: Nonlinear Auto-regressive Model With Exogenous Inputs (Narx)mentioning

confidence: 99%

Deep learning models for improved accuracy of a multiphase flowmeter

Manami

Seddighi

2023

Measurement

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Section: Nonlinear Auto-regressive Model With Exogenous Inputs (Narx)mentioning

confidence: 99%

Deep learning models for improved accuracy of a multiphase flowmeter

Manami

Seddighi

2023

Measurement

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“…Among the ML approaches, a non-linear autoregressive network with exogenous inputs (NARX) [18,19], i.e., a recurrent dynamic neural network used to model nonlinear Information 2023, 14, 417 2 of 16 dynamic systems and applied in time series, seems to be a promising method to perform signal analysis inherent to the internal combustion engine. Taghavi et al [20] compared the capability of a NARX structure to predict the start of combustion of a HCCI (homogeneous charge compression ignition) one-cylinder Ricardo engine with multi-layer perceptron (MLP) and radial basis function (RBF) networks.…”

Section: Introductionmentioning

confidence: 99%

NARX Technique to Predict Torque in Internal Combustion Engines

et al. 2023

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To carry out increasingly sophisticated checks, which comply with international regulations and stringent constraints, on-board computational systems are called upon to manipulate a growing number of variables, provided by an ever-increasing number of real and virtual sensors. The optimization phase of an ICE passes through the control of these numerous variables, which often exhibit rapidly changing trends over time. On the one hand, the amount of data to be processed, with narrow cyclical frequencies, entails ever more powerful computational equipment. On the other hand, computational strategies and techniques are required which allow actuation times that are useful for timely and optimized control. In the automotive industry, the ‘machine learning’ approach is becoming one the most used approaches to perform forecasting activities with reduced computational effort, due to both its cost-effectiveness and its simple and compact structure. In the present work, the nonlinear dynamic system we address is related to the torque estimation of an ICE through a nonlinear autoregressive with exogenous inputs (NARX) approach. Preliminary activities were performed to optimize the neural network in terms of neurons, hidden layers, and the number of input parameters to be assessed. A Shapley sensitivity analysis allowed quantification of the impact of each variable on the target prediction, and therefore, a reduction in the amount of data to be processed by the architecture. In all cases analyzed, the optimized structure was able to achieve average percentage errors on the target prediction that were always lower than a critical threshold of 10%. In particular, when the dataset was augmented or the analyzed cases merged, the architecture achieved average prediction errors of about 1%, highlighting its remarkable ability to reproduce the target with fidelity.

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“…This approach involving no systematic way of predefining the factor levels most often leads to a local optimum, but not necessarily a global optimum, as the optimum must always be determined from the predefined levels. Additionally, it is tedious and involves the generation of more data to identify the optimum levels, which may be unreliable [44,45]. In the statistical design of experiments, a wide variety of experimental conditions are tested, and mathematical models are used to generate predictions for responses outside the scope of the original experiment.…”

Section: Introductionmentioning

confidence: 99%

“…In the statistical design of experiments, a wide variety of experimental conditions are tested, and mathematical models are used to generate predictions for responses outside the scope of the original experiment. The results can then be subjected to statistical and numerical analyses that yield useful information for potential real-world applications [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology: The Improvement of Tropical Residual Soil Mechanical Properties Utilizing Calcined Seashell Powder and Treated Coir Fibre

et al. 2023

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Calcined seashell (CSS) powder and treated coir fibre (CF) are well-established additives for reinforcing poor soils. However, the absence of specific mix designs to optimize the mix additives makes it difficult to predict their combined effect on improving the mechanical behaviour of poor soils. This research explores the use of response surface methods to find the optimal proportions of CSS and CF for enhancing the mechanical properties of a tropical residual soil. This study uses a combination of Analysis of Variance (ANOVA) and regression models to examine how the independent variables of the CSS content, CF content, and curing duration influence the responses of the Unconfined Compressive Strength (UCS), Flexural Strength (FS), and Indirect Tensile Strength (ITS). The findings show that the optimal mix of 9.06% CSS, 0.30% CF, and 12 days of curing significantly improved the UCS, FS, and ITS by roughly six, four, and three times, respectively. Microstructural analysis revealed that the formation of calcium-aluminate-hydrate and calcium-silicate-hydrate are the primary components responsible for the enhanced mechanical properties of the treated soil.

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Forecasting the moisture dynamics of a landfill capping system comprising different geosynthetics: A NARX neural network approach

Cited by 10 publications

References 37 publications

Deep learning models for improved accuracy of a multiphase flowmeter

Deep learning models for improved accuracy of a multiphase flowmeter

NARX Technique to Predict Torque in Internal Combustion Engines

Response Surface Methodology: The Improvement of Tropical Residual Soil Mechanical Properties Utilizing Calcined Seashell Powder and Treated Coir Fibre

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