Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna, Sujay Raghavendra; Deka, Paresh Chandra; Ghorbani, Mohammad Ali; Biazar, Seyed Mostafa; Al‐Ansari, Nadhir; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.3390/w11040742

Cited by 77 publications

(28 citation statements)

References 51 publications

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“…Compared to other learning algorithms, such as back propagation (BP), ELM achieves swift learning and performs well in generation function processing [52][53][54][55]. Using ELM in various engineering science fields, such as feature selection [56], classification [57], and regression [51,58], has provided acceptable results.…”

Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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“…where Od is the output decision, ωj and ω0 represent the connection weights, and h is the hidden layer. The hidden layers are also connected to the output layers through a neural connection which holds the output weights [33,[71][72][73][74][75]. Initially, the weights of the connections hold random values until they intersect another connection-a phase in which they are multiplied by the associated weights and that intersection [34].…”

Section: Multilayer Perceptron Neural Network (Mlp)mentioning

confidence: 99%

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Popa

Peptenatu

Drăghici

et al. 2019

Water

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The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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“…, where x is the input parameters of the studied problem while y is the output parameter (K s ); both functions are expressed in the HD feature space; e represents the independent random error [50]. For a given dataset, G = {( ), I = 1, 2, …, l}, where l is the training data size, is the input value, and is the output value.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Reinforced concrete confinement coefficient estimation using soft computing models

Mohana¹

2019

PEN

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Infrastructure vulnerability toward seismic lateral loading within high seismicity has received massive attention by structural engineers and designers. This is for the purpose to provide a reliable alternative material that strengthening the bending and shear of slabs, columns and reinforced concrete (RC). Despite the utilized approaches of strengthening the concrete structure based on fiber reinforced polymers (FRP) is considerably new technique, exploring more reliable and robust methodologies is the motive of scholars for better structural behaviour understanding. In the current research, two soft computing models called artificial neural network (ANN) and support vector regression (SVR) are applied to predict lateral confinement coefficient (K s). The models are developed based on gathered dataset from open source researches for the lateral confinement coefficient of columns wrapped with carbon FRP (CFRP) and their corresponding parameters including column width, length and thickness (b, h and t mm), column radius (r mm), compressive strength of concrete () and elastic modulus (E FRP). Results indicated the superiority of the ANN model for predicting K s over the SVR model. The application of the soft computing showed an optimistic approach for the structural lateral confinement coefficient determination.

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Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Cited by 77 publications

References 51 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Reinforced concrete confinement coefficient estimation using soft computing models

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