scholarly journals The development of a regional geomagnetic daily variation model using neural networks

2000 ◽  
Vol 18 (1) ◽  
pp. 120-128 ◽  
Author(s):  
P. R. Sutcliffe
Keyword(s):  
Neural Networks ◽  
Geomagnetic Field ◽  
Daily Variation ◽  
Temporal Variations ◽  
African Region ◽  
Geomagnetic Field Models ◽  
Time Variations ◽  
Input Variables ◽  
First Time ◽  
Variation Model

Abstract. Global and regional geomagnetic field models give the components of the geomagnetic field as functions of position and epoch; most utilise a polynomial or Fourier series to map the input variables to the geomagnetic field values. The only temporal variation generally catered for in these models is the long term secular variation. However, there is an increasing need amongst certain users for models able to provide shorter term temporal variations, such as the geomagnetic daily variation. In this study, for the first time, artificial neural networks (ANNs) are utilised to develop a geomagnetic daily variation model. The model developed is for the southern African region; however, the method used could be applied to any other region or even globally. Besides local time and latitude, input variables considered in the daily variation model are season, sunspot number, and degree of geomagnetic activity. The ANN modelling of the geomagnetic daily variation is found to give results very similar to those obtained by the synthesis of harmonic coefficients which have been computed by the more traditional harmonic analysis of the daily variation.Key words. Geomagnetism and paleomagnetism (time variations; diurnal to secular) · Ionosphere (modelling and forecasting)

Time variations of directional cosmic ray intensity at low latitudes: I. Comparison of daily variation of the intensity of cosmic rays incident from east and west

1961 ◽  
Vol 263 (1312) ◽  
pp. 101-117 ◽  
Keyword(s):  
Geomagnetic Field ◽  
Daily Variation ◽  
Cosmic Ray ◽  
Local Source ◽  
Cosmic Ray Intensity ◽  
Low Latitudes ◽  
Long Term Changes ◽  
Time Variations ◽  
East And West

A study has been, conducted at Ahmedabad during 1957 and 1958 of the time variations of meson intensity incident from east and west at 45° to the vertical. A characteristic differ­ence of about 6 h in the diurnal time of maximum for the east and west directions is observed to occur on many days and this has been interpreted as signifying an anisotropy of primary radiation caused by a source outside the influence of the geomagnetic field. However, there are many days on which the daily variation has a maximum near noon for both directions. On such days the predominant influence is that of a local source situated within the influence of the geomagnetic field. The local source is associated with geomagnetically disturbed days. Long-term changes in the daily variation are found to be similar for the east, vertical and west directions.

scholarly journals Verification of the Geomagnetic Field Models Using Historical Satellite Measurements Obtained in 1964 and 1970

2022 ◽  
Author(s):  
Anatoly Soloviev ◽  
Dmitry Peregoudov
Keyword(s):  
Geomagnetic Field ◽  
Field Model ◽  
Digital Data ◽  
Mathematical Methods ◽  
Satellite Measurements ◽  
Data Set ◽  
Geomagnetic Field Models ◽  
Improved Model ◽  
First Time

Abstract In 2019, the WDC for Solar-Terrestrial Physics in Moscow digitized the archive of observations of the Earth’s magnetic field carried out by the Soviet satellites Kosmos-49 (1964) and Kosmos-321 (1970). As a result, the scientific community for the first time obtained access to a unique digital data set, which was registered at the very beginning of the scientific space era. This article sets out three objectives. First, the quality of the obtained measurements is assessed by their comparison with the IGRF reference field model. Secondly, we assess the quality of the models, which at that time were derived from the data of these two satellites and ground-based observations. Thirdly, we propose a new, improved model of the geomagnetic field secular variation based on the scalar measurements of the Kosmos-49 and Kosmos-321 satellites using modern mathematical methods.

scholarly journals Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Sabrina Sanchez ◽  
Johannes Wicht ◽  
Julien Bärenzung
Keyword(s):  
Magnetic Field ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Wave Number ◽  
Main Magnetic Field ◽  
Candidate Model ◽  
Uncertainty Estimates ◽  
Geomagnetic Field Models ◽  
Dipole Configuration

Abstract The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

scholarly journals Hydrostatic Densitometer for Monitoring Density in Freshwater to Hypersaline Water Bodies

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1842
Author(s):  
Ziv Mor ◽  
Hallel Lutzky ◽  
Eyal Shalev ◽  
Nadav G. Lensky
Keyword(s):  
Continuous Monitoring ◽  
Pressure Sensors ◽  
Temporal Variations ◽  
Dead Sea ◽  
Conductivity Measurements ◽  
Lake Levels ◽  
Density Profiles ◽  
Water Column Stratification ◽  
Hypersaline Water ◽  
First Time

Density, temperature, salinity, and hydraulic head are physical scalars governing the dynamics of aquatic systems. In coastal aquifers, lakes, and oceans, salinity is measured with conductivity sensors, temperature is measured with thermistors, and density is calculated. However, in hypersaline brines, the salinity (and density) cannot be determined by conductivity measurements due to its high ionic strength. Here, we resolve density measurements using a hydrostatic densitometer as a function of an array of pressure sensors and hydrostatic relations. This system was tested in the laboratory and was applied in the Dead Sea and adjacent aquifer. In the field, we measured temporal variations of vertical profiles of density and temperature in two cases, where water density varied vertically from 1.0 × 103 kg·m−3 to 1.24 × 103 kg·m−3: (i) a borehole in the coastal aquifer, and (ii) an offshore buoy in a region with a diluted plume. The density profile in the borehole evolved with time, responding to the lowering of groundwater and lake levels; that in the lake demonstrated the dynamics of water-column stratification under the influence of freshwater discharge and atmospheric forcing. This method allowed, for the first time, continuous monitoring of density profiles in hypersaline bodies, and it captured the dynamics of density and temperature stratification.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Comparison of <i>H</i> component geomagnetic field time series obtained at different sites over South America

2018 ◽  
Vol 36 (3) ◽  
pp. 937-943 ◽  
Author(s):  
Maurício J. A. Bolzan ◽  
Clezio M. Denardini ◽  
Alexandre Tardelli
Keyword(s):  
South America ◽  
Geomagnetic Field ◽  
Phase Coherence ◽  
South American ◽  
Data Set ◽  
Geomagnetism And Paleomagnetism ◽  
Strong Phase ◽  
Geomagnetic Fields ◽  
Time Variations ◽  
Physical Phenomena

Abstract. The geomagnetic field in the Brazilian sector is influenced by the South American Magnetic Anomaly (SAMA) that causes a decrease in the magnitude of the local geomagnetic field when compared to other regions in the world. Thus, the magnetometer network and data set of space weather over Brazil led by Embrace are important tools for promoting the understanding of geomagnetic fields over Brazil. In this sense, in this work we used the H component of geomagnetic fields obtained at different sites in South America in order to compare results from the phase coherence obtained from wavelet transform (WT). Results from comparison between Cachoeira Paulista (CXP) and Eusébio (EUS), and Cachoeira Paulista and São Luis (SLZ), indicated that there exist some phenomena that occur simultaneously in both locations, putting them in the same phase coherence. However, there are other phenomena putting both locations in a strong phase difference as observed between CXP and Rio Grande, Argentina (RGA). This study was done for a specific moderate geomagnetic storm that occurred in March 2003. The results are explained in terms of nonlinear interaction between physical phenomena acting in distinct geographic locations and at different times and scales. Keywords. Geomagnetism and paleomagnetism (time variations – diurnal to secular)

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

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