Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Dimopoulos, Ioannis; Tsiros, Ioannis X.; Serelis, K.; Chronopoulou, A.

doi:10.1080/10473289.2004.10471014

Cited by 11 publications

(2 citation statements)

References 34 publications

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“…All of these investigators used single ANNs to model PTFs, however, a few researchers are beginning to use ANN ensembles -combinations of ANNs -to develop PTF models. Publications include those by Schaap and Leij (1998a, b), Schaap et al (1998), Schaap et al (2001), Nemes et al (2003), Dimopoulos et al (2004), Jeong and Kim (2004), Minasny et al (2004), Quanet al (2004, Baker (2005), Parasuraman et al (2006) and Spencer et al (2006).…”

Section: Introductionmentioning

confidence: 97%

Optimisation of pedotransfer functions using an artificial neural network ensemble method

Baker¹,

Ellison

2008

Geoderma

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Section: Introductionmentioning

confidence: 97%

Optimisation of pedotransfer functions using an artificial neural network ensemble method

Baker¹,

Ellison

2008

Geoderma

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“…The present study deals with the application of artificial neural networks (ANNs), an alternative modeling technique against common modeling efforts for the evaluation and the prognosis of human thermal comfort conditions in the urban environment. During the last decade, there has been an increasing use of ANNs, in various aspects of the atmospheric environment studies [14][15][16][17][18][19][20]. Despite this increasing use of ANNs and their advantages (generalization properties, capability of handling high dimensional data, and nonlinearities), the number of ANNs model applications in problems related to bioclimatic aspects of human health is, however, still limited (e.g., [21,22]), whereas in the case of the urban environment studies, those focus only on the microclimate patterns [23].…”

Section: Introductionmentioning

confidence: 99%

One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

Moustris

Nastos

Παλιατσός

2013

Advances in Meteorology

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The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs) modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET) one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of < 0.01.

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Microarray Data Classified by Artificial Neural Networks

Linder

Richards

Wagner

2007

Methods in Molecular Biology

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Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis of high-throughput data as provided by DNA or protein microarrays. Along with the increasing availability of microarray data, attention is focused on methods of analyzing the expression rates. One important type of analysis is the classification task, for example, distinguishing different types of cell functions or tumors. Recently, interest has been awakened toward artificial neural networks (ANN), which have many appealing characteristics such as an exceptional degree of accuracy. Nonlinear relationships or independence from certain assumptions regarding the data distribution are also considered. The current work reviews advantages as well as disadvantages of neural networks in the context of microarray analysis. Comparisons are drawn to alternative methods. Selected solutions are discussed, and finally algorithms for the effective combination of multiple ANNs are presented. The development of approaches to use ANN-processed microarray data applicable to run cell and tissue simulations may be slated for future investigation.

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Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Abstract: ABSTRACTand 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

Cited by 11 publications

References 34 publications

Optimisation of pedotransfer functions using an artificial neural network ensemble method

Optimisation of pedotransfer functions using an artificial neural network ensemble method

One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

Microarray Data Classified by Artificial Neural Networks

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