Predicting the physical parameters of interplanetary shock waves using Artificial Neural Networks trained on NASA’s ACE and WIND spacecrafts

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Download Full-text

Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water

Food and Chemical Toxicology ◽

10.1016/j.fct.2018.04.036 ◽

2018 ◽

Vol 118 ◽

pp. 212-219 ◽

Cited By ~ 16

Author(s):

Marjan Salari ◽

Esmaeel Salami Shahid ◽

Seied Hosein Afzali ◽

Majid Ehteshami ◽

Gea Oliveri Conti ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Quality Assessment ◽

Potable Water ◽

Physical Parameters ◽

Artificial Neural

Download Full-text

Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1405-2016 ◽

2016 ◽

Vol 20 (4) ◽

pp. 1405-1412 ◽

Cited By ~ 22

Author(s):

Yabin Sun ◽

Dadiyorto Wendi ◽

Dong Eon Kim ◽

Shie-Yui Liong

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Numerical Models ◽

Groundwater Resources ◽

Computational Cost ◽

Groundwater Table ◽

Physical Parameters ◽

Swamp Forest ◽

Freshwater Swamp ◽

Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

Download Full-text

Using simulation to define the tolerances for the information and physical parameters of memristors-based artificial neural networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1333/6/062026 ◽

2019 ◽

Vol 1333 ◽

pp. 062026 ◽

Cited By ~ 2

Author(s):

S N Danilin ◽

S A Shchanikov ◽

I A Bordanov ◽

A D Zuev

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Physical Parameters ◽

Artificial Neural

Download Full-text

Technical Note: Application of artificial neural networks in groundwater table forecasting – a case study in Singapore swamp forest

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-9317-2015 ◽

2015 ◽

Vol 12 (9) ◽

pp. 9317-9336 ◽

Cited By ~ 1

Author(s):

Y. Sun ◽

D. Wendi ◽

D. E. Kim ◽

S.-Y. Liong

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Numerical Models ◽

Groundwater Resources ◽

Computational Cost ◽

Technical Note ◽

Groundwater Table ◽

Physical Parameters ◽

Swamp Forest ◽

Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a swamp forest of Singapore. A standard multilayer perceptron (MLP) is selected, trained with the Levenberg–Marquardt (LM) algorithm. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce accurate forecast with a leading time up to 7 days, whereas the performance slightly decreases when leading time increases.

Download Full-text