Computational neural networks for predictive microbiology: I. methodology

Najjar, Yacoub M.; Basheer, Imad A.; Hajmeer, Maha N.

doi:10.1016/s0168-1605(96)01168-3

Cited by 82 publications

(39 citation statements)

References 10 publications

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“…Choosing the number of middle layers (hidden) is the most crucial item in the ANN structure. As previous research has shown (Cybenko 1989;Hornik et al 1989;Najjar et al 1997 andShahin et al 2002), one hidden layer is sufficient to approximate any continuous function, provided that sufficient connection weights are given.…”

Section: Ann Structure Set-upmentioning

confidence: 97%

Forecasting watermain failure using artificial neural network modelling

Asnaashari

McBean

Gharabaghi

et al. 2013

Canadian Water Resources Journal

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After rapid urban expansion in Ontario, post-World War II, there followed a lengthy period of time where only minimal infrastructure maintenance occurred. Now, however, most of that infrastructure is approaching the end of its predicted life expectancy, and has started failing at an unprecedented rate. The combination of low maintenance and the increasing age of water distribution infrastructure has resulted in increasing rates of pipe failures. To assign priorities for repair/ replacement, artificial neural network modelling is employed. Eight independent variables are employed, namely pipe length, diameter, age, break category, soil type, pipe material, the year of Cement Mortar Lining (if implemented), and the year of Cathodic Protection (if implemented), to determine the importance of different factors influencing the pipe failure rate. The results in application to the distribution system in Etobicoke, Ontario demonstrate that ANN models have very strong predictive capabilities (R 2 =0.94) when compared with the multiple linear regression method (R 2 =0.75) to assist rehabilitation planning.Après la rapide expansion urbaine qui suivi la seconde guerre mondiale, l'Ontario connu une longue période pendant laquelle on ne porta attention qu'à l'entretien des petites infrastructures. Maintenant, la plupart des infrastructures approchent de leur fin de vie, et ont commencé à se détériorer à un rythme sans précédent. La combinaison du faible entretien et du vieillissement des infrastructures de distribution de l'eau a entraîné une augmentation des taux de bris des conduites. Pour constituer un outil d'aide à la décision, essentiel dans le choix du réseau à réhabiliter en priorité, on cartographie la prévision des défaillances du réseau de distribution d'eau à l'aide du système de modélisation des réseaux neuronaux artificiels (RNA). Cette approche a été appliquée au réseau de la ville de Etobicoke dans l'Ontario. Le modèle comporte huit variables indépendantes, notamment: longueur de la conduite, diamètre, âge, matériau, catégorie des défaillances, type de sol, plus deux facteurs de travaux de réhabilitation. Aux canalisations, on inclut l'année de mortier du ciment de revêtement (s'il a été appliqué), et l'année de la protection cathodique (si elle est appliquée). Afin de déter-miner l'importance des différents facteurs qui influencent les défaillances des conduites. Les résultats obtenus pour le réseau d'eau à Etobicoke, démontrent que les modèles RNA ont de très fortes capacités de prévision (R 2 = 0.94) pour faciliter les stratégies de réhabilitation, par rapport à la méthode de régression linéaire multiple (R 2 = 0.75).

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Section: Ann Structure Set-upmentioning

confidence: 97%

Forecasting watermain failure using artificial neural network modelling

Asnaashari

McBean

Gharabaghi

et al. 2013

Canadian Water Resources Journal

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“…It is a powerful model to establish the complexity of the inputoutput relations of a system [18]. The neural network simulates the principle of functioning of the human brain which manages an information flow from a learning database [22].…”

Section: Overview Of Formal Neural Networkmentioning

confidence: 99%

“…The coefficient of determination (R 2 ), the mean square error (RMSE), the coefficient of Nash-Sutcliffe (NA), and the coefficient of correlation (r) have been used to compare the performance of the models and choose the best. The criterion of Nash-Sutcliffe varies from -∞ to 1 and the following scale is generally used [18].…”

Section: Evaluation Of the Performance Of The Modelsmentioning

confidence: 99%

Application of Neural Modeling and the SPI Index for the Prediction of Weather Drought in the Saïss Plain (Northern Morocco)

Ibrahimi¹,

Baali²

2017

IJIES

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This contribution will verify the effectiveness of formal neural networks for predicting drought in a semiarid region using a hybrid model of formal neural networks (ANN-MLP) and the standardized precipitation index ( SPI). Three types of models have been optimized to achieve this objective. A database consisting of SPI values, rain, temperature and potential evapotranspiration (PET) at the monthly time step was used as input for these models. These data have been standardized between 0 and 1 and subdivided into two blocks: a first block composed of 2/3 of the data for learning and a second block composed of 1/3 of the data for the test and the validation of the models. These models have been optimized with supervised learning. The activation function chosen is the logistic variant of the type sigmoid. The mean square error (RMSE), the correlation coefficient (R), the criterion of Nash-Sutcliffe (Nash) and the absolute mean error (MAE) were used to test the performance of these models. The results obtained show that the 3rd model is the most efficient. The application of neural networks for the estimation of the dryness of the Saïss Plain yielded quite good results. Indeed, the coefficients of correlation between the predicted and the measured values range from 0.63 to 0.97. It is therefore noted that the performances obtained are relatively good and could be improved by using a larger database.

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“…The authors of [21] argue that a single hidden layer of neurons, operating a sigmoidal activation function, is adequate for modeling any solution surface of practical interest. In some applications, one hidden layer is commonly used [22]. Analyses presented in the paper were conducted using one or two hidden layers.…”

Section: The Presentation Of the Ann Approachmentioning

confidence: 99%

Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks

Zabada

Shahrour

2017

Energies

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This paper presents an analysis of heating expenses in a large social housing stock in the North of France. An artificial neural network (ANN) approach is taken for the analysis of heating consumption data collected over four years in 84 social housing residences containing 13,179 dwellings that use collective heating. Analysis provides an understanding of the influence of both physical and socio-economic parameters on heating expenses and proposes a predictive model for these expenses. The model shows that the heating expenses are influenced by both the buildings' physical parameters and social indicators. Concerning the physical parameters, the most important indicators are the area of the dwellings, followed by the building age and the DPE (energy performance diagnostic). The family size as well as tenant age and income have an important influence on heating expense. The model is then used for establishing a data-based strategy for social housing stock renovation.

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Computational neural networks for predictive microbiology: I. methodology

Cited by 82 publications

References 10 publications

Forecasting watermain failure using artificial neural network modelling

Forecasting watermain failure using artificial neural network modelling

Application of Neural Modeling and the SPI Index for the Prediction of Weather Drought in the Saïss Plain (Northern Morocco)

Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks

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