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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Applying Gmdh-Type Neural Network And Particle Warm Optimization For Prediction Of Liquefaction Induced Lateral Displacements, Reza A. Jirdehi, Hamidreza T. Mamoudan, Hossein H. Sarkaleh
Applying Gmdh-Type Neural Network And Particle Warm Optimization For Prediction Of Liquefaction Induced Lateral Displacements, Reza A. Jirdehi, Hamidreza T. Mamoudan, Hossein H. Sarkaleh
Applications and Applied Mathematics: An International Journal (AAM)
Lateral spreading and flow failure are amongst the most destructive effects of liquefaction. Estimation of the peril of lateral spreading requires characterization of subsurface conditions, principally soil density, fine content, groundwater conditions, site topography and seismic characteristics. In this paper a GMDH-type neural network and particle swarm optimization is developed for prediction of liquefaction induced lateral displacements. Using this method, a new model was proposed that is suitable for predicting the liquefaction induced lateral displacements. The proposed model was tested before the requested calculation. The data set which is contains 250 data points of liquefaction-induced lateral ground spreading case histories …
Integrated Remote Sensing And Forecasting Of Regional Terrestrial Precipitation With Global Nonlinear And Nonstationary Teleconnection Signals Using Wavelet Analysis, Lee Mullon
Electronic Theses and Dissertations
Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals …
Remote Sensing With Computational Intelligence Modelling For Monitoring The Ecosystem State And Hydraulic Pattern In A Constructed Wetland, Golam Mohiuddin
Remote Sensing With Computational Intelligence Modelling For Monitoring The Ecosystem State And Hydraulic Pattern In A Constructed Wetland, Golam Mohiuddin
Electronic Theses and Dissertations
Monitoring the heterogeneous aquatic environment such as the Stormwater Treatment Areas (STAs) located at the northeast of the Everglades is extremely important in understanding the land processes of the constructed wetland in its capacity to remove nutrient. Direct monitoring and measurements of ecosystem evolution and changing velocities at every single part of the STA are not always feasible. Integrated remote sensing, monitoring, and modeling technique can be a state-of-the-art tool to estimate the spatial and temporal distributions of flow velocity regimes and ecological functioning in such dynamic aquatic environments. In this presentation, comparison between four computational intelligence models including Extreme …
An Artificial Neural Network Approach For Sensorless Speed Estimation Via Rotor Slot Harmonics, Hayri̇ Arabaci
An Artificial Neural Network Approach For Sensorless Speed Estimation Via Rotor Slot Harmonics, Hayri̇ Arabaci
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a sensorless speed estimation method with an artificial neural network for squirrel cage induction motors is presented. Motor current is generally used for sensorless speed estimation. Rotor slot harmonics are available in the frequency spectrum of the current. The frequency components of these determined harmonics are used to estimate the speed of the motor in which the number of rotor slots is given. In the literature, individual algorithms have been used to calculate the speed from the slot harmonics. Unlike the literature, in the proposed method, an artificial neural network is used to extract the speed from …
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong
Research outputs 2014 to 2021
This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, …