Exploring The Parameter Space Of Unsupervised ART Neural Networks For Data Mining

Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Hydrological models are widely used to characterise, understand and manage hydrosystems. Data-driven models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of data-driven modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two data-driven modelling approaches: artificial neural networks (ANN) and reservoir models. We investigate five karst systems in the Mediterranean and Alpine regions with different characteristics in terms of climatic conditions, hydrogeological properties and data availability. We compare the results of ANN and reservoir modelling approaches using several performance criteria over different hydrological periods. The results show that both ANN and reservoir models can accurately simulate karst spring discharge, but also that they have different advantages and drawbacks: (i) ANN models are very flexible regarding the format and amount of input data, (ii) reservoir models can provide good results even with short calibration periods, and (iii) ANN models seem robust for reproducing high-flow conditions while reservoir models are superior for reproducing low-flow conditions. However, both modelling approaches struggle to reproduce extreme events (droughts, floods), which is a known problem in hydrological modelling. For research purposes, ANN models have shown to be useful to identify recharge areas and delineate catchment, based on insights into the input data. Reservoir models are adapted to understand the hydrological functioning of a system, by studying model structure and parameters.

<strong class="journal-contentHeaderColor">Abstract.</strong> Hydrological models are widely used to characterise, understand and manage hydrosystems. Data-driven models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of data-driven modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two data-driven modelling approaches: artificial neural networks (ANN) and reservoir models. We investigate five karst systems in the Mediterranean and Alpine regions with different characteristics in terms of climatic conditions, hydrogeological properties and data availability. We compare the results of ANN and reservoir modelling approaches using several performance criteria over different hydrological periods. The results show that both ANN and reservoir models can accurately simulate karst spring discharge, but also that they have different advantages and drawbacks: (i) ANN models are very flexible regarding the format and amount of input data, (ii) reservoir models can provide good results even with short calibration periods, and (iii) ANN models seem robust for reproducing high-flow conditions while reservoir models are superior for reproducing low-flow conditions. However, both modelling approaches struggle to reproduce extreme events (droughts, floods), which is a known problem in hydrological modelling. For research purposes, ANN models have shown to be useful to identify recharge areas and delineate catchment, based on insights into the input data. Reservoir models are adapted to understand the hydrological functioning of a system, by studying model structure and parameters.

—An artificial neural network (ANN) model is recommended for the analysis and prediction of the output results of a recently designed and constructed generator known as a permanent magnet generator with axial flux. The generator is considered to generate a power scale of P = 3 kW, especially for off-grid household usage far from the network. This machine has an adjustable air gap mechanism, and P = 3 kW is the maximum power; thus, this power can be decreased comfortably by increasing the air gap further to 7 mm. In this case, the maximum power rate can be decreased to P = 1.5 kW according to the experimental confirmation. As a new technique, an ANN approach is introduced for the predictions of voltage and power obtained output for various electrical loads, rotor speeds, and air gaps because it is difficult to get output values for all those machine parameters. In addition, this ANN technique enables one to find the generated voltage and power for specific extremely low air gap values (i.e., 0.2 mm, 0.4 mm, 0.6 mm), which is hard in experimental work due to construction difficulties. The ANN model is used to predict the output values of the generator before any laboratory practice successfully, and the users can determine the correct air gap ranges to their required power regimes for this generator.

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

Improving productivity in construction projects has long been a major concern, and much research has been carried out to try to ameliorate construction productivity. To this end, this study aims to improve and increase the productivity rate of flat slab formwork used in residential construction projects. A survey consisting of 150 questionnaires was undertaken to identify the factors that influence on the productivity. Based on the relative Importance Index (RII), data on eleven factors deemed to affect productivity were selected. A collection of 100 data points from various sites were utilized to develop two models. Firstly, an Artificial Neural Network (ANN) model was employed, and secondly, a parametric approach was investigated. The data were divided into two sets, with 70% of the data used for training and the remaining 30% used for testing. The models’ performance was evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. In the test phase, the artificial neural network model yielded an MSE value of 2.6610e−4 and a MAPE value of 4.9227, whereas the parametric model produced an MSE of 0.040 and a MAPE of 9.525. It was found that the artificial neural network model provided reliable prediction accuracy compared to the parametric model. However, the artificial neural network approach can be selected as a robust model in predicting and controlling the productivity rate in local construction projects by using the developed model based on the identified factors.

This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg–Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R2) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy.

When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R2), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R2 for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data.

BackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.ResultsFor distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.ConclusionsOur study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.

The objective of this paper is to assess the optimum heat load capacity of a real CHP plant and provide recommendations to improve heat utilization and reduce costs using a computerized system. A simulation model based on component actual behaviour has been developed. The simulation model is capable of plant optimization that could lead to significant economic and energy consumption improvements. The general modular structural of the plant component is described together with a discussion of the results and cost analysis. In the second part, feasibility of the Artificial Neural Network (ANN) approach is evaluated. The data from the simulation model of the plant is used to train such an ANN model. Results from the conventional computer technique are compared with that of the direct method based ANN approach. The results indicate it is feasible to use ANN to predict plant-operating conditions. The ANN gives a good time response and performance prediction capability with change of boundary conditions. Significantly shorter computation time is obtained with the ANN compared to the physical model. The accuracy of the ANN output and its suitability for on-line monitoring of a CHP plant are discussed.

Artificial neural network model for predicting the local compression capacity of stirrups-confined concrete

BackgroundThe transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.Methodology/Principal FindingsWe aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected.Conclusion/SignificanceBoth BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.

ABSTRACTStatistical methods such as Poisson distribution, negative binomial regression (NB), and zero inflated negative binomial regression (ZINB) have generally been used in road safety studies to establish the complex relationships between variables. Over the last few years, the artificial neural networks (ANN) model has also been used. The ANN model does not have any prior limitations such as the equality condition of mean and variance observed in Poisson regression. However, though the ANN model has been used in the analysis of different accident types, to the best of our knowledge, no study has used the ANN model for establishing this relationship with truck accident data on divided multilane interurban roads. In this study, the road sections D750/07–D750/15 in Ankara–Aksaray–Eregli, Turkey, were considered and truck accident data from 2008 to 2011 were analyzed using NB and ANN. The analysis show that the ANN model has lower errors and higher R2 values than NB and performs slightly better than NB for predicting the number of trucks involved in accidents. Based on a comparison of performances the study concludes, that ANN could be used as an alternative model for analyzing truck accident data on divided multilane interurban roads in Turkey.